Spatial Modulation for MIMO Wireless Systems
Marco Di Renzo(1), Harald Haas(2) and Ali Ghrayeb(3)
(1) Laboratory of Signals and Systems (L2S), CNRS – SUPÉLEC – University of Paris-Sud XI 3 rue Joliot-Curie, 91192 Gif-sur-Yvette, France marco.direnzo@@plss.supelec.fr
(2) The University of Edinburgh, Institute for Digital Communications (IDCOM) Mayfield Road, Edinburgh, EH9 3JL, UK hhh.haas @e d.ac.u k
(3) Concordia University, Department of Electrical and Computer Engineering 1455 de Maisonneuve West, Montreal, H3G 1M8, Canada [email protected] WCNC, EW, VTC-Spring, ICC, VTC-Fall, CAMAD 2013
IEEE Tutorial Presented at: http://www.fp7-greenet.eu 2 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO .
Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 3 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO .
Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 4 (1+SNR) 2 )log r ,N t -(NtNr) Array gain (beamforming), spatial division multiple access Spatial multiplexing: Rate = min(N Reliability: BEP ~ SNR
Why MIMO? , 5 y lexit number p py the with linearly Why? . decreases stems is the increased com : RF elements are expensive, bulky, no y complexity efficiency (IAS): Detection algorithms require that all (ICI): Introduced by coupling multiple symbols in energy (RF) chains y processing The : signal – space of conventional MIMO s consumption k and simple to implement, and do not follow Moore’s law. Inter-channel interference time Inter-antenna synchronization symbols are transmitted at the sameMultiple time. radio frequenc Energy of active antennas(>60%) (RF – chains) EARTH model. and it mostly depends on the Power Amplifiers Regardless of thedrawbac use as diversityincreased or power/energy spatial consumption, and multiplexing high system, cost. the main
Very Good, But… Very 6 IEEE Commun. ”, Single RF Front-End MIMO Transceivers , Vol. 49, No. 12, pp. 104-109, Dec. 2011. Conventional MIMO Single-RF MIMO Conventional vs. Single-RF MIMO Single-RF vs. Conventional A. Mohammadi and F. M. Ghannouchi, “ Mag. 7 , Vol. 13, No. 4, pp. 524-540, Nov. 2011. Green Cellular Networks: A Survey, Some Research IEEE Commun. Surveys & Tutorials ”,
The Energy Efficiency (EE) Challenge (1/3) Issues and Challenges Z. Hasan, H. Boostanimehr, and V. K. Bhargava, “ 8 BS Power Consumption , Vol. 13, No. 4, pp. 524-540, Nov. 2011. Green Cellular Networks: A Survey, Some Research IEEE Commun. Surveys & Tutorials ”,
The Energy Efficiency (EE) Challenge (2/3) Issues and Challenges Z. Hasan, H. Boostanimehr, and V. K. Bhargava, “ : 9 IEEE VTC- ”, R2-094677 from Huawei ", 2011. y eNB power saving by changing antenna number Theoretical and Practical Considerations for the Design of Green Radio Networks , Budapest, Hungary, Ma g
The Energy Efficiency (EE) Challenge (3/3) http://www.3gpp.org/ftp/tsg_ran/WG2_RL2/TSGR2_67/Tdoclist/History/ADN_Tdoc_List_RAN2_67.htm. 3GPP TSG-RAN WG2 #67, " S. D. Gray, “ 2011 Sprin 10 , vol. 60 n. 5, pp. 1345-1356, May 2012. On the energy efficiency-spectral efficiency trade-off over the IEEE Trans. Commun. ”, MIMO Gain WITHOUT Considering Circuit Power
Static Power: How Much Is It Important How Power: ?(1/2) Static F.Heliot,M.A.Imran,andR.Tafazolli,“ MIMO rayleigh fading channel 11 , vol. 60 n. 5, pp. 1345-1356, May 2012. On the energy efficiency-spectral efficiency trade-off over the IEEE Trans. Commun. ”, MIMO Gain Considering Circuit Power
Static Power: How Much Is It Important How Power: ?(2/2) Static F.Heliot,M.A.Imran,andR.Tafazolli,“ MIMO rayleigh fading channel 12 ith the w ,vol.49,no.6, .Ithasbeen s: . g i . EE is the noise power e 0 h scenarios y, t l SE SE
SE IEEE Commun. Mag. various 21 ”, 0 for onsequent N lead to transmittin Cl C s. y 0 b/ P NB BR perspective R 2 2 = 2 21 hich ma 2 C w 0 s i N , y theoretic t i - P CR 12 log 1 12 capac l EE constraints R anne information r hl h the ec h owe p Fundamental Tradeoffs on Green Wireless Networks from e ore, t f g ere SE-oriented systems areavera designed to maximizemaximum the allowed power capacity for under long periods, peakEE thus is or deviate commonly from defined EEstudied as design. information bits per unitFor of an transmit additive energy whitegiven transmit Gaussian power, P, noise and system (AWGN) bandwidth, channel, B, it the channel is capacity well is: known that for a bits per real dimension or degrees ofTh freedom (DOF), where f N It follows that the EE decreases monotonically with R (i.e., with SE) spectral density. According to the Nyquist sampling theory, DOF per second is 2B.
SE vs. EE Tradeoff (1/2) Y. Chen et al., “ pp. 30–37, June 2011. 13 IEEE ", , Vol. 18, No. 6, pp. 28-35, Dec. 2011. Energy-Efficient Wireless Communications: Tutorial, Survey, and Open Issues
SE vs. EE Tradeoff (2/2) G. Y. Li et al., " Wireless Commun. Mag. d 14 ng an i ormance f ex l er ty i p p i t lil l i ex li l e ibl g li ibl
or mu ) f ng comp neg SM di ( h t eco ih i ddi d ements w l (PSK) e-stream l ng atial Modulation t-antenna e on il i i i p p() s S at h l t ih i u dld i transm ll has the inherent potential to meet these goals ta i ope mo l o ormance w li l f er degradation Having one (orexp few) activetransmit-diversity RF gains chains but still being able to Offering Maximum-Likelihoodp (ML) optimumWorking decoding withoutmodulation schemes the (QAM)enve need or allowing of us to (power use constant- inefficient) linear
Now, Imagine a New Modulation for MIMOs: Now, l l 15 a a i ti pa pat Stil S Sil S Diversity Transmit Modulation Multiplexing S1 S1 S1 S2 S2 0 1 * * S1 -S2 ime T Block - ace- p Time Vertical - Coding Spatial S2 = 0/1 Orthogonal ered S Modulation y yp Bell Laboratories Bell Laboratories La Space S1 S1 S1 S2 S2 SM – In a Nutshell S2 16 Re Tx0
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10 Im Spatial Modulation for Multiple-Antenna Wireless Systems - A , Vol. 49, No. 12, pp. 182-191, December 2011. Signal Constellation for Tx3 (Tx1) 01 (Tx2) 10 Im (Tx3) 11 Spatial Constellation IEEE Communications Magazine ”,
SM – (3D Constellation Diagram) It Works How Survey M. Di Renzo, H. Haas, and P. M. Grant, “ 17 Re Tx0
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10 Im Spatial Modulation for Multiple-Antenna Wireless Systems - A , Vol. 49, No. 12, pp. 182-191, December 2011. Signal Constellation for Tx3 (Tx1) 01 (Tx2) 10 Im (Tx3) 11 Spatial Constellation IEEE Communications Magazine ”,
SM – (1/3) It Works How … 1110 0001 … 0001 … 1110 Survey M. Di Renzo, H. Haas, and P. M. Grant, “ 18 Re Tx0
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10 Im Spatial Modulation for Multiple-Antenna Wireless Systems - A , Vol. 49, No. 12, pp. 182-191, December 2011. Signal Constellation for Tx3 (Tx1) 01 (Tx2) 10 Im 0001 … (Tx3) 11 10 Spatial Constellation IEEE Communications Magazine 11 ”,
SM – (2/3) It Works How … Survey M. Di Renzo, H. Haas, and P. M. Grant, “ 19 Re Tx0
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10 Im Spatial Modulation for Multiple-Antenna Wireless Systems - A , Vol. 49, No. 12, pp. 182-191, December 2011. Signal Constellation for Tx3 (Tx1) 01 … (Tx2) 10 01 Im 00 (Tx3) 11 Spatial Constellation IEEE Communications Magazine ”,
SM – (3/3) It Works How … 1110 Survey M. Di Renzo, H. Haas, and P. M. Grant, “ 20 (M) 2 og l + )l ) t Transmitter (BPSK) (N
2 SIGNAL 1(BPSK) 1 - SELECTION og l 1 001010100111010111 … 1 10 SM MAPPER BINARY SOURCE BINARY 10 … 100101011100110010 Tx2 ANTENNA SELECTION
SM – Transmitter 21 x3 Communication Channel T Tx3 Tx2 Tx2 Rx Tx1 Wireless Channel Tx1 Tx0 Tx0
SM – Wireless Channel } 22 (±) Di { Receiver Compute min ) 2) x x3 T2) T T +Tx1) - , , x,- Rx (R ( (, stance distance(Rx di (R
Detection = distance(Rx,+Tx0) = distance(Rx,+Tx2) = distance(Rx,+Tx3) = distance(Rx +Tx1) = distance(Rx,-Tx0) = = distance = distance(Rx,-Tx1) ) ) - + (+) (-) (+) ( (+) (-) ( () (-) D2 D0 D1 D1 D2 D3 D0 D3 Rx a priori CSI
SM – Receiver . 23 g) (M) 2 lexin tradeoff ? p pg) ? )+log t ons (N ractical? ti 2 -multi p l ca SE/EE iti? i atia p s good ( (p a p antennas for y ave commun en-loo Or W p mm- the end-to-end performance. In SM, t (M) bpcu – SM: log 2 MIMO? - ou .SMiso bt b y) a log t EE t a is SM more sensitive to channel estimation , For Wh t ? s MIMO? - MIMO ? transmit-diversit ( SE mechanism ve i p g for mass t (M) bpcu – MIMO: N ou 2 looking bt b a t we a AS is closed-loo antenna switching depends on the incoming bit-stream. T In TAS, antenna switching depends on Correct. Butconsumption, and what energy efficiency about (EE)? Are signal processing complexity, cost, total power What does fair mean? Same transmit-antennas?Wh Same RF t chains? No, it is as/more robust as/than MIMO and we have results proving it. What is the difference with Transmit Antenna Selection (TAS)? SIMO: log SM needs manysame more SE. Is transmit-antennas the than comparison fair? conventional Is MIMO having so for man the Due to the encodin errors than conventional MIMO? bpcu. So, SM is spectral efficiency (SE) sub-optimal. Why using it?
Common Misunderstandings : 24 MIMO d ze li (large–scale enera illars: Glid G p or . f number of active ” on i reducing the circuit increase both the SE at l y u , July 2013 (to appear). [Online]. d to o r MdlM i diagram l on two main a i p pat Sil S “ in orde anzo, H constellation . - increase the EE b L d elements Proceedings of the IEEE to – ”, r spatial ura, an “ i networks is based u r ug (single–RF MIMO principle). Si S the . in orde S , by b raye assive antenna p Gh b . , given some implementation and size constraints, the , given some performance constraints, the elements A of – r efficient cellula aas, introduced H gy . H power consumption numbe Minimize antenna Maximize and the EE byMIMO reducing principle). the This transmit isgain power realized consumption by capitalizing on the multiplexing enzo, 1) 2) R The rationale behind SM–MIMOand communications ener for the design of spectral Di
Our Proposal: Single-RF Large-Scale SM-MIMO Our Proposal: Single-RF . M Challenges, Opportunities and Implementation Available: http://hal.archives-ouvertes.fr/docs/00/84/02/78/PDF/ProcIEEE_SM_FullPaper.pdf. , ”, 25 2011 Spring meeting . ”, imited Numbers of Base Station Antennas outube.com/watch?v=U3euDDr0uvo y . w http://ww , vol. 9, no. 11, pp. 3590-3600, Nov. 2010. vailable at: A Noncooperative Cellular Wireless with Unl GreenTouch Initiative: Large Scale Antenna Systems Demonstration
Massive MIMO (1/5) Massive G. Wright “ Seoul, South Korea. T. L. Marzetta, “ IEEE Trans. Wireless Commun. , a 26 es, y bl stem. ,sa y power ca y Scaling up l antennas a il i low ,vol.30,no.1, of ufvesson, “ built toda T affect the s . y g F arge coax l extremely as number h reciabl uses pp y stems bein IEEE Signal Proces. Mag. y unit ”, tems, suc i ould not a lky w antenna unprecedented u blk b .L.Marzetta,O.Edfors,and T d an each , ve an i entails systems expens l .Lau,E.G.Larsson, K MIMO MIMO nitude more elements than in s g a few of the antenna units r large large onus, severa of ma b r very sa of the order of mW. With very large MIMO,orde we think of systemshundred that antennas or use more. antenna arrays with an Very simultaneously serving a much smaller number of terminals. In A Very-large MIMO designs canof be one made o extremelyMalfunctioning robust individual antennas in may that be the hotswapped. failure can be eliminatedbase altogether. stations today (The are coaxial up to cables four used centimeters in for diameter). tower-mounted
Massive MIMO (2/5) Massive .Rusek,D.Persson,B. http://www.commsys.isy.liu.se/~egl/vlm/vlm.html. F MIMO: Opportunities and Challenges with Very Large Arrays pp. 40–46, Jan. 2013. ”, 27 defeat all detection y neighboring user in . of pilots forms ould eventuall w transmitters using - r re simplest imited Numbers of Base Station Antennas from the , arising antennas interference from othe of y , vol. 9, no. 11, pp. 3590-3600, Nov. 2010. interference by number limited b y limited Noncooperative Cellular Wireless with Unl infinite an redominantl noise and fading, and eliminate both intra-and inter-cell interference. The main effectnoise of and scaling fast up fadingp the can dimensions be is averaged that out uncorrelated and thermalIf vanish so we that could thecell assign then system an large is orthogonal numbers pilot of sequence base to station every antennas terminal inBut every there are notsequences enough orthogonal have pilotbecomes to sequences for be allcells terminals. reused. (pilot Pilot contamination problem). The performanceWith of aand precoding, very i.e., matched largeoptimal. filtering (MF) array and eigenbeamforming, become Spectral efficiencytransmitted energy per is bit vanishes. independent of bandwidth, and the required
Massive MIMO (3/5) Massive T. L. Marzetta, “ IEEE Trans. Wireless Commun. ”, 28 : cell x per imited Numbers of Base Station Antennas x kk users and const 1 K K k NK y H mm and yhn h 1 N BS , vol. 9, no. 11, pp. 3590-3600, Nov. 2010. per Noncooperative Cellular Wireless with Unl antennas N ance. Uncorrelated interference and noise vanish The matched filter is optimal The transmit power can be made arbitrarily small i hus, with an unlimited number of BS antennas: ar Consider a MIMOwith Multiple Access (MAC - UPLINK) system where channel and noisev are i.i.d. RVs with zeroBy mean the and strong unit law of large numbers: T
Massive MIMO (4/5) –Massive In Formulas T. L. Marzetta, “ IEEE Trans. Wireless Commun. ”, 29 rovide the same vanish p is limited by pilot j receivers m use the same pilot: estimation noise estimation j xx imited Numbers of Base Station Antennas estimation errors ion and and , j m and const noise NK , pilot contaminat pilot y H mm ˆ h minimum mean square 1 N , vol. 9, no. 11, pp. 3590-3600, Nov. 2010. mm and ˆ hh h n r Noncooperative Cellular Wireless with Unl contamination Uncorrelated interference The performance of the matchedMatched filte filter receiver limiting performance Assume now that transmitter Thus, by the strong law of large numbers: Thus, with an unlimited number of BS antennas:
Massive MIMO (5/5) –Massive In Formulas T. L. Marzetta, “ IEEE Trans. Wireless Commun. 30 circuits power -active: multi-RF MIMO y transmit-antennas ) hundreds or more ( y transmit-antennas are simultaneousl l l Many (hundreds or more) transmit-antennas A but antennas are less expensive andEE: more reduction EE of than state-of-the-art transmit (RF) power Man One (or few) transmit-antennasRF are MIMO simultaneously-active: single- EE: reduction of transmit (RF) power and Massive MIMO: SM-MIMO:
Massive MIMO vs. SM-MIMO (downlink) Massive , . ut 31 p total This . source from the y ith filters and largest limited out w the differentl is , chains tion of a y p amplifier power MISO solution the some antennas in general are turned off under an assum hole radio frequenc w turn off antennas while operating optimally that . Amplifier–aware multiple–input multiple–output power agrees , is such that as maximized to turn off w eobservethatour y : W , vol. 17, no. 6, pp. 1112-1115, June 2013. literature ossibilit p schemes. information Message on: l i g scientific ut beneficial in cases where dissipated power per antenna is significant ib The mutua IEEE Commun. Lett. . However, in many applications it is desirable to instead limit the . ”, we utilize the analytic expression of amplifier losses to design MIMO ontr Motivation: …the power consumed power, consistingchain of bothof output losses power in the and transmitter. losses in theCibi C transmitter … traditional MRT beamforming Takeaway Theproposedprocedureallowsusto also gives us the mixers, which saves additional power which is beamformin
Power-Amplifier Aware MISO Design MISO Aware Power-Amplifier allocation D. Persson, T. Eriksson, and E. Larsson, “ l r a i 32 and pat have Sil S ,“ d ave-vecto w element R. Prasa d RF as, an di active -field in the r .B.Papa single C a , i , and to encode independent ett hi h with compact parasitic architectures gains s, N. Marc li variations of the fa A novel approach to MIMO transmission using a single RF r .Ka , vol. 26, no. 6, pp. 972–980, Aug. 2008. A . The key idea is to change the radiation pattern ragas, , vol. 15, no. 2, pp. 178–180, Feb. 2011. T multiplexing . P ne, h enable varat to at each symbol time instance Di h . C , di IEEE Commun. Lett. a IEEE J. Select. Areas Commun. b ”, ”, ra proposed Al b di
Transmission Concepts Related to Concepts SM (1/5) Related Transmission .N. of the array New multiple antenna designsbeen based on many passive antenna elements information streams onto angula domain. front end A. Kalis, A. G. Kanatas, and C. B.O Papadias, “ multiplexing with a singleprototypes radio: Proof–of–concept experiments in an indoor environment with a 2.6 GHz . y to 33 b y data avoid to efficienc incorrect y of Incremental MIMO proposed case in been have transmitter , vol. 60, no. 9, pp. 2522-2533, Sep. 2012. and to improve the energ the y at feedback , thus enabling MIMO gains with a single RF (ARQ) antennas Incremental use of multiple transmitters for low-complexity diversity IEEE Trans. Commun. ,” reQuest available the Repeat . through he main idea is to reduce complexit Transmission Concepts Related to Concepts SM (2/5) Related Transmission chain and a single power amplifier. This solution is named New MIMO schemesAutomatic jointly combiningkeep multiple-antenna all transmission available antennas and on T having one active antennacycle at a time, butreception to exploit ARQ feedback to randomly P. Hesami and J. N. Laneman, “ transmission in wireless systems 34 an The . for subset of have been symbol communications received . The main idea in ASM is by driving only a the of , vol. 61, no. 8, pp. 3231-3245, Aug. 2013. wireless mm-Wave frequencies phase switching antenna subsets does not for y and Antenna subset modulation for secure millimeter-wave complexity - low and amplitude hile randoml W the . y secure IEEE Transactions on Communications Antenna Subset Modulation (ASM) “, enable randomizes to directional modulation schemes modulate the radiation pattern at the symbol rate
Transmission Concepts Related to Concepts SM (3/5) Related Transmission affect the symbol modulation for a desired receiver along the main direction, it New proposed solution is named antennas in the arra effectively eavesdropper along a sidelobe. to wireless communication N. Valliappan, A. Lozano, and R. W. Heath Jr., " 35 , vol. 61, no. 8, pp. 3231-3245, Aug. 2013. Antenna subset modulation for secure millimeter-wave IEEE Transactions on Communications “,
Transmission Concepts Related to Concepts SM (4/5) Related Transmission wireless communication N. Valliappan, A. Lozano, and R. W. Heath Jr., " – 36 low– cost of recoding in antenna . For this p one or a few sparse . large .Proposal: y Spatiall where , submitted, May 2013. [Online]. schemes .HeathJr.,“ W precoding Surra, Z. Pi, and R. IEEE Trans. Wireless Commun. – bu ”, A RF/baseband hybrid yach,S.Rajagopal,S. A
Transmission Concepts Related to Concepts SM (5/5) Related Transmission In Millimeter–wave Mobile Broadband (MMB)reason, system analog baseband design, beamforming the or RF beamformingcomplexity with arrays are driven by a limited number of transmit/receive chains implementing one RF chain per transmit–antenna canactive be RF prohibitive chains can be promising low–complexity solutions Available: http://arxiv.org/pdf/1305.2460.pdf. O. El millimeter wave MIMO systems : 37 MIMO d ze li enera Glid G or f on i at l u , July 2013 (to appear). [Online]. d o MdlM i l a i pat Sil S “ anzo, amplifiers H . y L l d Proceedings of the IEEE pp y ”, power ura, an i ug the Si S . S , ower su of b p raye Gh b . A aas, H efficiency . H enzo, R Higher throughput Simpler receiver design Simpler transmitter design Lower transmit Better Di
To Summarize: SM-MIMO Advantages… Summarize: To . M Challenges, Opportunities and Implementation Available: http://hal.archives-ouvertes.fr/docs/00/84/02/78/PDF/ProcIEEE_SM_FullPaper.pdf. : 38 MIMO d ze li s) n enera o Glid G or ati f c on li i at l u pp c o s) , July 2013 (to appear). [Online]. d a o MdlM i l ve a i a pat Sil S W Wve “ anzo, H rmm . o L f d ( (o Proceedings of the IEEE ”, g ura, an i ug Si S rmin . S o , b raye amf Gh b be o g . A l overhead aas, na g H o . ti H ec o enzo, rainin R Spectral efficiency sub-optimality Fast antenna switching Time-limited pulse shaping Favorable propagation conditions T Dir Di
… and Some Disadvantages/Trade-Offs . M Challenges, Opportunities and Implementation Available: http://hal.archives-ouvertes.fr/docs/00/84/02/78/PDF/ProcIEEE_SM_FullPaper.pdf. 39 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO .
Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. , ” 40 Using Wireless Channels 3 Rate 201 / /3 MIMO Data Multiplexing 2012 for Data High by for , "Practical Implementation of 2011 Modulation Hopping 9 Efficiency - 200 Keying Spectral Channel Shift 2008 , “Spatial Modulation for Multiple-Antenna Wireless Space Guided . “ - , . Increasing , “Spatial modulation - A New Low Complexity Spectral “ Mag al , . et , “Spatial Modulation”, IEEE Trans. Veh. Technol. , Schultz Information “ . Commun , E A Ghrayeb , “Space Modulation on Wireless Fading Channels”, IEEE VTC-Fall . Jiao A . , “A Channel Hopping Technique I: Theoretical Studies on Band Efficiency IEEE , B Costa, , ” . E and IEEE ISC , ” Survey N. Serafimovski, A. Younis, M. Di Renzo, H. Haas, et al. y Haas, A Yang Jeganathan . - acit . . R. Y. Mesleh, H. Haas, et al. J p py, S. Song, et al. Y H M.DiRenzo,H.Haas,P.M.Grant R. Y. Mesleh, H. Haas, et al. Y. Chau, S.-H. Yu 2001 2002 2004 2006 ] ] ]
A Glimpse into the HistoryA Glimpse of SM 2002 2008 2009 IEEE Trans. Wireless Commun. [2011] and Ca [2001] [ Antennas Arrays”, IEEE PIMRC [2004] [2006] [ Communication”, IEEE Commun. Lett. [2008] [ Systems Efficiency Enhancing Technique”, ChinaCom Spatial Modulation", IEEE Trans. Veh. Technol., (to appear, IEEE Early Access) [2012/2013] 41 groups) y i) rc i Chockalingam) . anay A Pii) P . E and asar, Hari B . . S . (E V y . Bahrami) e . k (K R . ur other universities, China (man Tk T y (H India ty, i , US vers i and man n UiU i Science y Akron, l of ca of i n h of Southampton, UK (L. Hanzo) ec y Thil T l Institute u b University singhua Universit stan University of Edinburgh, UK (H. Haas) CNRS – SUPELEC – University ofConcordia Paris-Sud University, Canada XI, (A. France (M. Ghrayeb) Di Renzo) University of Tabuk, Saudi Arabia (R.Universit Y. Mesleh) Princeton University, US (V. Poor) Ibl I Tokyo University, Japan (S. Sugiura) Indian Québec University - INRS, CanadaThe (S. Aissa) Academia Sinica, Taiwan (a large group) T Le Quy Don Technical University, Vietnam (T.etc., X. etc., Nam) etc… Collaborations with: Univ.(UK), of Heriot-Watt Univ. L’Aquila (UK), EADS (Italy), (Germany), etc… CTTC (Spain), Univ. of Bristol
Research Groups Working on SM GroupsResearch Working 42 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO .
Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 43 IEEE Trans. Veh. ”, bpcu
3 bpcu Spatial modulation tt ls x Spatial Modulation (SM) Spatial Modulation , vol. 57, no. 4, pp. 2228-2241, July 2008.
Transmitter Design –Transmitter Encoding (1/7) R.Y.Mesleh,H.Haas,S.Sinanovic,C.W.Ahn,andS.Yun,“ Technol. 44 diagram Constellation - Space shift keying modulation for MIMO l Spatial the t l by more efficient power amplifiers (no linearity Hx n h n , vol. 8, no. 7, pp. 3692-3703, July 2009. only y Space Shift Keying (SSK) conveyed ) is IEEE Trans. Wireless Commun. No signal modulation Simplified demodulation Larger antenna-arrays are needed for the same spectral efficiency constraints ”, Information
Transmitter Design –Transmitter Encoding (2/7) J. Jeganathan, A. Ghrayeb, L. Szczecinski, and A. Ceron, “ channels 45 one n a th n more g in w llo in a b by off - ded a tr ded er time slots: p be n a cn c Generalized SM and SSK Generalized SSK ity r x o (M) bpcu for SM f 2 r rates are: i pcu e b hi h )+log ) comple it t t (N (N nd 2 2 a og log l Generalized SSK Generalized SM Variable Generalized SSK/SM te owever, t a SM and SSK aregreatly simplifies appealing the because transmitter. ofH their single RF design which Rte R active antenna in eachnumbers time of instance, active as antennas well as by allowing different
Transmitter Design –Transmitter Encoding (3/7) 46 3bpcu t t = n N
= 5 = 2 t 2 t n Rate N Rate log Generalized space shift keying modulation for MIMO Generalized SSK (GSSK) Generalized , pp. 1–5, 2008. IEEE PIMRC ,”
Transmitter Design –Transmitter Encoding (4/7) J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “ channels 47 t t Asilomar n N M ,” cu 2 2 p log = 5 = 2 t t n Rate = 4b = Rate N BPSK Rate log Generalized Spatial Modulation , Pacific Grove, CA, USA, 2010. Generalized SM (GSM) Generalized
Transmitter Design –Transmitter Encoding (5/7) A. Younis, N. Serafimovski, R. Mesleh, and H. Haas, “ Conference on Signals, Systems, and Computers 48 t (M) 2 = 4 = 1 and 2 t t t N MQAM/MPSK N Rate+ log = 3bpcu n t N t t t N n N 2 1 t t N n t t n N 0 t N t n Variable Generalized SSK/SM (VGSM/VGSSK) Generalized Variable MMMN g VGSM 2VGSSK 2 2 2 2
Transmitter Design –Transmitter Encoding (6/7) Rate logRate log lo log 2 1 log 1 49 Complexity GSM Complexity SM antennas 2 - R = transmit kh of Hx Hx number t n Q Reasoning on the Tradeoffs on the Reasoning chains) PEP SNR (RF active . vs Performance Signal processing complexity (detection) Total
Transmitter Design –Transmitter Encoding (7/7) 50 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO .
Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 51 IEEE Trans. Veh. diagram) ”, F 2 F 2 s l H l ˆ H l h hy constellation - yh Spatial modulation l (signal s arg max arg min symbol ˆ s ˆ l modulated the of : Detection Detection Detection of the antenna index (spatial-constellationDetection diagram) antenna-index , vol. 57, no. 4, pp. 2228-2241, July 2008. modulated-symbol The firstapproach proposed demodulator for SM is based on a two-step
Receiver Design –Receiver Demodulation (1/12) R.Y.Mesleh,H.Haas,S.Sinanovic,C.W.Ahn,andS.Yun,“ Technol. 52 d e d eco ddd d ly F 2 F 2 nt i o , jij l ls ls yhx yHx agrams are di Spatial modulation: Optimal detection and performance on , i ls , at ls ll i arg min arg min SM -conste l ˆ , vol. 12, no. 8, pp. 545-547, Aug. 2008. , gna ˆ il i ls s d -an l a i pat Sil S IEEE Commun. Lett. ”, Maximum-Likelihood (ML) optimum decoding:
Receiver Design –Receiver Demodulation (2/12) J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “ analysis s, 53 SNR Compressed ,vol.16,no.10, um di me / ow l/di l or f IEEE Commun. Lett. SM ", to Generalized Sphere Decoding for Spatial ormance f Sensing er p f egoo , Vol. 61, No. 7, pp. 2805–2815, July 2013. id Compressed rov of p y e . th 2. 01 application ct. 2 mes, O IEEE Trans. Commun. , ti 9 ”, The The application of Sphere Decoding to SM 55 19 1 - 6 ome while they performance degrades for high SNRs. Many other sub-optimal demodulators have been proposed recently. In general,performance they offer aSti S trade-off between complexity and We consider two examples: 55 16 1
Receiver Design –Receiver Demodulation (3/12) p. C.-M.Yu,S.-H.Hsieh,H.-W.Liang,C.-S.Lu,W.-H.Chung,S.-Y.Kuo,andS.-C.Pei," p Sensing Detector Design for Space Shift Keying in MIMO Systems Modulation A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ e 54 h t on: i Compressed " at l ,vol.16,no.10, u dld i mo SSK f o .Kuo,andS.-C.Pei, IEEE Commun. Lett. y Y t ", i t n aconvexprogramviaCS rt rtt .-H. Chung, S.- W erent spars ONN h ONNn n ih i instead of 2-norm of ML demodulation e h ML : CS: .Liang,C.-S.Lu, W everage t l sto i ) Compressed Sensing (CS) Generalized Space Shift KeyingCompressedSensing (CS) Generalized ea t id Receiver Design –Receiver Demodulation (4/12) Sensing Detector Design for Spacepp. 1556-1559, Shift Oct. Keying 2012. in MIMO Systems C.-M. 55 the Compressed " ,vol.16,no.10, .Kuo,andS.-C.Pei, IEEE Commun. Lett. Y ", t 1 n x .-H. Chung, S.- Φ 1 W trt y NNN arg min ˆ xx ,, , 11 ows: rr ll NN o .Liang,C.-S.Lu, fll f CR C C W is a zero/one vector with one entries yHxn yxH n x on, as i zat ) Compressed Sensing (CS) Generalized Space Shift KeyingCompressedSensing (CS) Generalized t i m i can be re-constructed with high probability by 1-norm Receiver Design –Receiver Demodulation (5/12) Sensing Detector Design for Spacepp. 1556-1559, Shift Oct. Keying 2012. in MIMO Systems C.-M. s y i 56 are est .If g y ea T . Φ id x Ф Compressed e " ,vol.16,no.10, Th . and the lar l ith zero mean and w can be obtained b (OMP) Ф t i . y .Kuo,andS.-C.Pei, IEEE Commun. Lett. Y ", ursu t orthonorma t Pi P n N y ng robabilit 2 p py hi g h lo should be chosen as follows: g .-H. Chung, S.- atc by computing the correlation r is nearl robability, matrix W x p Mhi M Ф l ith hi T w Ф r rt ogona NOn ith high h then w correspond to the non-zero coefficients of rt , y .Liang,C.-S.Lu, OhO l that satisfies the Restricted Isometric Property (RIP). CS T elements from a Normal distribution . The RIP ensures that pairs of columns of t r W Ф ×N r says that, onal to each othe ors use g y h Compressed Sensing (CS) Generalized Space Shift KeyingCompressedSensing (CS) Generalized is an N : ortho theor variance 1/N Ф generating its The number of observations N eaut satisfies the RIP u, S.-H. Hsieh, H.- Y where Th coefficients of find the non-zero elements of Ф Receiver Design –Receiver Demodulation (6/12) Sensing Detector Design for Spacepp. 1556-1559, Shift Oct. Keying 2012. in MIMO Systems C.-M. 57 real 8 requires 2 s , h rr y distance || Generalized Sphere Decoding for Spatial 1 r y-h N r rinciple: p rt M M n t t i|i | N N 8 12 12 { , ,..., } { , ,..., } {1,2,..., } {1,2,..., } arg m Euclidean sss s sss s ML CNNM , Vol. 61, No. 7, pp. 2805–2815, July 2013. each ss ML ML tt F ()() 2 Sphere Decoding (SD) Spatial Modulation [, ]argmin|||| evaluating IEEE Trans. Commun. ”, since Optimum detector based on the ML Computational complexity of ML (real multiplications): multiplications Receiver Design –Receiver Demodulation (7/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 2 58 rN 2 RN examining only y ,2 1 rr r NN N Generalized Sphere Decoding for Spatial , Vol. 61, No. 7, pp. 2805–2815, July 2013. Sphere Decoding (SD) Spatial Modulation IEEE Trans. Commun. ”, he SD algorithm avoids an exhaustive search b those points that lie inside a sphere of radius R: T Receiver Design –Receiver Demodulation (7/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 59 ace p , s , , h yhs y yhs || 1 Generalized Sphere Decoding for Spatial (,) 1 r r 1 r (,) N r Ns r r the receive search s Ns roposed and studied: } R g M p t n i N R s , ,..., (,) 12 (,) (,) s {} { {1,2,..., } rr rr arg m NsN (,) NsN sss s ] s , Vol. 61, No. 7, pp. 2805–2815, July 2013. s s , Tx-SD Tx-SD tt rr ()() 2 C-SD C-SD tt rr ()() 2 Rx-SD Rx-SD , ]rmn | [, ]argmin| tt rr ()() 2 []i|| [ , ]rmn | [, ]argmin| Sphere Decoding (SD) Spatial Modulation , which aims at reducin , which aims at reducing the transmit search space , which aims at reducing both transmit and receive search IEEE Trans. Commun. Rx-SD Tx-SD C-SD spaces ”, hree sphere decoders for SM are 1. 2. 3. T Receiver Design –Receiver Demodulation (8/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 60 s 22 (,) s as long as it is 21 (,) (l,s) oint p Generalized Sphere Decoding for Spatial aths leading to each , Vol. 61, No. 7, pp. 2805–2815, July 2013. p ) s , 12 () ( Sphere Decoding (SD) Spatial Modulation – Rx-SD IEEE Trans. Commun. ”, SD searches the s – 11 (,) Rx antenna still inside the sphere when adding up the signals at each receive- Receiver Design –Receiver Demodulation (9/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 61 , hs y || 1 r N r Generalized Sphere Decoding for Spatial R min ) s , g () ( , Vol. 61, No. 7, pp. 2805–2815, July 2013. s Tx-SD Tx-SD tt rr ()() 2 Sphere Decoding (SD) Spatial Modulation – Tx-SD [, ]ar IEEE Trans. Commun. ”, Receiver Design –Receiver Demodulation (10/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 62 ve i ece ve r e h t te , educe hs r to y || 1 used (,) Generalized Sphere Decoding for Spatial r r s Ns is computed R hm i Θ t R ) ri min , g () ( go s l rr a ago t NsN (,) orithm is used to reduce the transmit search computed in Step 1 g D R S , Vol. 61, No. 7, pp. 2805–2815, July 2013. Θ – step detector that works as follows: – SD al Rx – x e s h T t te d, Sphere Decoding (SD) Spatial Modulation – C-SD C-SD C-SD n tt rr ()() 2 SD is a two [, ]ar IEEE Trans. Commun. – First, the space. The subset of points Seco d, search space. Moresubset specifically, of Rx–SD points is applied only on the ”, . he C 1. 2 T Receiver Design –Receiver Demodulation (11/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 63 ) , ) ( () , R Generalized Sphere Decoding for Spatial s M s 8 card{ } (,) 11 t 8 N R 8() 8( R : is SD is: SD , Vol. 61, No. 7, pp. 2805–2815, July 2013. – – Rx SD r Cx CNs Tx SD r r CSD r CCN of Rx of CC Ns y Sphere Decoding (SD) Spatial Modulation – C-SD IEEE Trans. Commun. ”, complexity he complexit T The complexity of Tx–SD is: The Receiver Design –Receiver Demodulation (12/12) A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 64 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 65 IEEE Trans. Veh. ”, Spatial modulation 6bpcu – i.i.d. Rayleigh fading , vol. 57, no. 4, pp. 2228-2241, July 2008. Error Performance –(1/24) Numerical Results R.Y.Mesleh,H.Haas,S.Sinanovic,C.W.Ahn,andS.Yun,“ Technol. 66 IEEE Trans. Veh. ”, Spatial modulation 8bpcu – i.i.d. Rayleigh fading , vol. 57, no. 4, pp. 2228-2241, July 2008. Error Performance –(2/24) Numerical Results R.Y.Mesleh,H.Haas,S.Sinanovic,C.W.Ahn,andS.Yun,“ Technol. 67 IEEE Trans. Veh. ”, Spatial modulation 6bpcu – 3GPP channel model , vol. 57, no. 4, pp. 2228-2241, July 2008. Error Performance –(3/24) Numerical Results R.Y.Mesleh,H.Haas,S.Sinanovic,C.W.Ahn,andS.Yun,“ Technol. 68 IEEE Trans. Veh. ”, Spatial modulation 8bpcu – 3GPP channel model , vol. 57, no. 4, pp. 2228-2241, July 2008. Error Performance –(4/24) Numerical Results R.Y.Mesleh,H.Haas,S.Sinanovic,C.W.Ahn,andS.Yun,“ Technol. 69 =4 r Spatial modulation: Optimal detection and performance , vol. 12, no. 8, pp. 545-547, Aug. 2008. 3bpcu – i.i.d. Rayleigh fading – N IEEE Commun. Lett. ”, Error Performance –(5/24) Numerical Results J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “ analysis 70 =4 r Space shift keying modulation for MIMO , vol. 8, no. 7, pp. 3692-3703, July 2009. 3bpcu – i.i.d. Rayleigh fading – N IEEE Trans. Wireless Commun. ”, Error Performance –(6/24) Numerical Results J. Jeganathan, A. Ghrayeb, L. Szczecinski, and A. Ceron, “ channels 71 =2 r Space shift keying modulation for MIMO , vol. 8, no. 7, pp. 3692-3703, July 2009. 1bpcu and 3bpcu – i.i.d. Rayleigh fading – N IEEE Trans. Wireless Commun. ”, Error Performance –(7/24) Numerical Results J. Jeganathan, A. Ghrayeb, L. Szczecinski, and A. Ceron, “ channels 72 =1, 2, 4 r Space shift keying modulation for MIMO , vol. 8, no. 7, pp. 3692-3703, July 2009. 1bpcu and 3bpcu – i.i.d. Rayleigh fading – N IEEE Trans. Wireless Commun. ”, Error Performance –(8/24) Numerical Results J. Jeganathan, A. Ghrayeb, L. Szczecinski, and A. Ceron, “ channels 73 =4 r Generalized space shift keying modulation for MIMO 3bpcu – i.i.d. Rayleigh fading – N , pp. 1–5, 2008. IEEE PIMRC ,” Error Performance –(9/24) Numerical Results J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “ channels 74 3 = t Asilomar , n M = 2 ,” , 12 = 8, M 2 t = = 8, M = 2 t t = 128 t N : SM: N GSM N VGSM: N SMX: N =4 Generalized Spatial Modulation r , Pacific Grove, CA, USA, 2010. 8bpcu – i.i.d. Rayleigh fading – N Error Performance –(10/24) Numerical Results A. Younis, N. Serafimovski, R. Mesleh, and H. Haas, “ Conference on Signals, Systems, and Computers 75 3 = t Asilomar , n M = 2 ,” , 12 = 8, M 2 t = = 8, M = 2 t t = 128 t N : =4 r SM: N GSM N VGSM: N SMX: N =0.6) – N β Generalized Spatial Modulation , Pacific Grove, CA, USA, 2010. Error Performance –(11/24) Numerical Results –8bpcu Rayleighcorrelation exponential fading, ( A. Younis, N. Serafimovski, R. Mesleh, and H. Haas, “ Conference on Signals, Systems, and Computers 76 3 = t Asilomar , n M = 2 ,” , 12 = 8, M 2 t = = 8, M = 2 t t = 128 t N : SM: N GSM N VGSM: N SMX: N Generalized Spatial Modulation =4 r , Pacific Grove, CA, USA, 2010. 8bpcu –8bpcu Rician fading i.i.d. – N Error Performance –(12/24) Numerical Results A. Younis, N. Serafimovski, R. Mesleh, and H. Haas, “ Conference on Signals, Systems, and Computers 77 3 = t Asilomar , n M = 2 ,” , 12 = 8, M 2 t = = 8, M = 2 t t = 128 t N : =4 r SM: N GSM N VGSM: N SMX: N =0.6) – N β Generalized Spatial Modulation , Pacific Grove, CA, USA, 2010. 8bpcu – Rician exponential correlationfading, ( Error Performance –(13/24) Numerical Results A. Younis, N. Serafimovski, R. Mesleh, and H. Haas, “ Conference on Signals, Systems, and Computers 78 Compressed r ,vol.16,no.10, = 256 = 1 t arying N t n N V .Kuo,andS.-C.Pei," IEEE Commun. Lett. Y ", .-H. Chung, S.- W .Liang,C.-S.Lu, W i.i.d. Rayleigh fading u, S.-H. Hsieh, H.- Y Error Performance –(14/24) Numerical Results C.-M. Sensing Detector Design for Spacepp. 1556-1559, Shift Oct. Keying 2012. in MIMO Systems 79 = 16 = 24 r r Compressed ,vol.16,no.10, = 2, N t = 3, N t : ” 2 2: “ = 256, n = 64, n t t Setup N Setup “3”: N .Kuo,andS.-C.Pei," IEEE Commun. Lett. Y ", .-H. Chung, S.- W .Liang,C.-S.Lu, W i.i.d. Rayleigh fading u, S.-H. Hsieh, H.- Y Error Performance –(15/24) Numerical Results C.-M. Sensing Detector Design for Spacepp. 1556-1559, Shift Oct. Keying 2012. in MIMO Systems 80 = 16 = 20 = 24 = 30 r r r r : Compressed ” ,vol.16,no.10, = 2, N = 2, N 20 : - t t = 3, N = 3, N t t CS2 “ = 256, n = 256, n = 64, n = 64, n t t t t Setup “CS2-16”: N Setup N Setup “CS3-24”: N Setup “CS3-30”: N .Kuo,andS.-C.Pei," IEEE Commun. Lett. Y ", .-H. Chung, S.- W .Liang,C.-S.Lu, W i.i.d. Rayleigh fading u, S.-H. Hsieh, H.- Y Error Performance –(16/24) Numerical Results C.-M. Sensing Detector Design for Spacepp. 1556-1559, Shift Oct. Keying 2012. in MIMO Systems 81 =4 r M=64 =4, N t Generalized Sphere Decoding for Spatial M=8 , Vol. 61, No. 7, pp. 2805–2815, July 2013. i.i.d. Rayleigh fading– N IEEE Trans. Commun. ”, Error Performance –(17/24) Numerical Results A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 82 16 = M16 M =2 r =2, N t Generalized Sphere Decoding for Spatial 8 = M8 M , Vol. 61, No. 7, pp. 2805–2815, July 2013. i.i.d. Rayleigh fading– N IEEE Trans. Commun. ”, Error Performance –(18/24) Numerical Results A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 83 64 = M64 M =4 r =4, N t Generalized Sphere Decoding for Spatial 8 = M8 M , Vol. 61, No. 7, pp. 2805–2815, July 2013. i.i.d. Rayleigh fading– N IEEE Trans. Commun. ”, Error Performance –(19/24) Numerical Results A.Younis,S.Sinanovic,M.DiRenzo,andH.Haas,“ Modulation 84 64 = M64 M =8 r =8, N t Generalized Sphere Decoding for Spatial 32 = M32 M , Vol. 61, No. 7, pp. 2805–2815, July 2013. i.i.d. Rayleigh fading– N IEEE Trans. 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APEP TX TX 0 12 12 k 4 As m ssBsBG 1,2 k A Ms s Error Performance – (2/38) Trends Main ABEP ABEP , APEP TX TX Modulation for MISO Correlatedpp. 2590-2603, Nakagami-m Sep. Fading 2010. Channels M. Di Renzo and H. Haas, “ 91 Error Performance – (3/38) Trends Main 92 Error Performance – (4/38) Trends Main 93 Error Performance – (5/38) Trends Main 94 Error Performance – (6/38) Trends Main 95 Error Performance – (7/38) Trends Main 96 Error Performance – (8/38) Trends Main 97 Error Performance – (9/38) Trends Main 98 Error Performance – (10/38) Trends Main 99 5 . 2 = 8 = = 1 t N m=25m Ω , Vol. 15, No. 10, pp. 1026-1028, Oct. 2011. Bit Error Probability of Space Modulation over Nakagami-m Fading: IEEE Commun. Lett. ”, Error Performance – (11/38) Trends Main M. Di RenzoAsymptotic Analysis and H. Haas, “ , 100 John Wiley & Sons, Inc. , Digital Communication over Fading Channels Error Performance – (12/38) Trends Main M. K. Simon and M.–S. 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Rayleigh Fading Bit Error Probability of Spatial Modulation (SM-) MIMO over Generalized IEEE Trans. Veh. Technol. ”, Error Performance – (25/38) Trends Main M. Di Renzo andFading Channels H. Haas, “ 114 Error Performance – (26/38) Trends Main 115 Error Performance – (27/38) Trends Main 116 Error Performance – (28/38) Trends Main 117 Error Performance – (29/38) Trends Main 118 Error Performance – (30/38) Trends Main 119 Error Performance – (31/38) Trends Main 120 Error Performance – (32/38) Trends Main 121 Error Performance – (33/38) Trends Main 122 Error Performance – (34/38) Trends Main 123 Error Performance – (35/38) Trends Main 124 Error Performance – (36/38) Trends Main 125 Error Performance – (37/38) Trends Main 126 Error Performance – (38/38) Trends Main 127 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 128 2 2 r R n 2 2 ≥ Tk , no. 6, pp. 311-335, Mar. 1998. 2 2 t n )] 2 2 Tn [1 ] / 2 g 1 [1 ( / ) ] ( 2 [1 ] = 2 = n r g 1 r T g n 1 i lo 2 lo Tni g[ ( ) ] = n, n = 1, n t t lo (1) T n C TR (1 / ) lo Cn kn n On limits of wireless communication in a fading environment when using Cn ty case: n i Cn vers Wireless Personal Commun.: Kluwer Academic Press Di ,” t i ransm Receiver Diversity case: n Diversity Receiver TiDii T n Diversity: Receiver and Combined Transmit using one transmitted at a time: Cycling Achievable Capacity (1/5) Achievable G. J. Foschini and M.multiple antennas J. Gans, “ ”, 129 2 22 y 2 mX N y h m m exp fyh fyh 22 g 2 1 lo m 2 m mX N t hd h 1 fy 1 SM 1 2 1 t y N g m CN CCC 1 t t lo N 1 11 m t N N t m Information-guided channel-hopping for high data rate wireless communication , vol. 12, no. 4, pp. 225–227, Apr. 2008. 1 t Nfy 1 N 22 fy 12 C Ch Achievable Capacity (2/5) Achievable Y. Yang and B. Jiao, “ IEEE Commun. Lett. ”, 130 Information-guided channel-hopping for high data rate wireless communication , vol. 12, no. 4, pp. 225–227, Apr. 2008. Achievable Capacity (3/5) Achievable Y. Yang and B. Jiao, “ IEEE Commun. Lett. ”, 131 Information-guided channel-hopping for high data rate wireless communication , vol. 12, no. 4, pp. 225–227, Apr. 2008. Achievable Capacity (4/5) Achievable Y. Yang and B. Jiao, “ IEEE Commun. Lett. ”, 132 Information-guided channel-hopping for high data rate wireless communication , vol. 12, no. 4, pp. 225–227, Apr. 2008. Achievable Capacity (5/5) Achievable Y. Yang and B. Jiao, “ IEEE Commun. Lett. 133 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 134 rove the p im y imbalance ma r 2 , Vol. 14, No. 6, June 2010. owe 2 p l and anne 2 and 1 4 hl h , 221 11 ng c di EEE N a fdi f IEEE Commun. Lett. h ”, 12 statistics g i EE Improving the Performance of Space Shift Keying (SSK) Modulation via e 11 2 2 1 2 12 0 l l E ay Rlih R 222 d ABEP , e t 2 a l E =2 ≠ t 1 orre N Cltd C E ireless channe The performancew of SSK/SMperformance modulation significantlyCan depends power imbalance be on created via the opportunistic power allocation? Assumptions: Channel State Information at the Transmitter (1/22) Channel State Information at the Transmitter M. Di Renzo,Opportunistic Power H. Allocation Haas, “ 135 0 2 2 2 M 2 1 σ σ , 2 = = 2 2 M M 22 σ σ av E )and g av ,0) and av min ABEP , 12 12 EE g 22 EE ar dB )=(2E )=(0,2E * * 2 2 SSK OOSSK 1 2 1 2 , ,g , ,E ,E * * 12 12 1 1 EE EE subject to: SNR (E (E SNR 2 2 2 1 2 gain 10 σ σ > > 2 2 1 2 SNR 10lo σ σ If If Channel State Information at the Transmitter (2/22) Channel State Information at the Transmitter 136 SSK Channel State Information at the Transmitter (3/22) Channel State Information at the Transmitter 137 OOSSK Channel State Information at the Transmitter (4/22) Channel State Information at the Transmitter 138 ) ,vol.64,no.4,pp.1623– Space Modulation with CSI: ran, " T vectors airs of constellation vectors at p ) these of IEEE Trans. Veh. Technol. MSSK ", pairs ng: i ey no constraint on the structure of the transmit Ki K : how to design the transmit vectors using , Multi-Antenna Space Modulation: MSMod between Shift pace S d distance e difi o Mdifid M In the firstvectors is imposed method ( In the second method, the( transmit vectors have only one non-zero entry The symbol errorEuclidean rate (SER) performance highlyOptimization depends problem onCSIT the such that the distance between Two methods are proposed: the receiver is larger Channel State Information at the Transmitter (5/22) Channel State Information at the Transmitter M.Maleki,H.R.Bahrami,S.Beygi,M.Kafashan,andN.H. Constellation Design and Performance1634, May Evaluation 2013. 139 ,vol.64,no.4,pp.1623– Space Modulation with CSI: ran, " T IEEE Trans. Veh. Technol. ", MSMod with Full-CSIT Channel State Information at the Transmitter (6/22) Channel State Information at the Transmitter M.Maleki,H.R.Bahrami,S.Beygi,M.Kafashan,andN.H. Constellation Design and Performance1634, May Evaluation 2013. 140 H ) constellations ,vol.64,no.4,pp.1623– =H+N Space Modulation with CSI: error ran, " H PSK/QAM T IEEE Trans. Veh. Technol. conventional ", H from of value chosen be can singular k MSMod with Full-CSIT: Optimal Solution with Full-CSIT: MSMod θ : line largest the is the right singular vector related to the largest singular value of is a constant is 1 ’ 1 v ε λ Bottom Similar results apply to the imperfect CSIT case ( Channel State Information at the Transmitter (7/22) Channel State Information at the Transmitter M.Maleki,H.R.Bahrami,S.Beygi,M.Kafashan,andN.H. Constellation Design and Performance1634, May Evaluation 2013. 141 ,vol.64,no.4,pp.1623– Space Modulation with CSI: ran, " T IEEE Trans. Veh. Technol. ", MSSK with Full-CSIT Channel State Information at the Transmitter (8/22) Channel State Information at the Transmitter M.Maleki,H.R.Bahrami,S.Beygi,M.Kafashan,andN.H. Constellation Design and Performance1634, May Evaluation 2013. 142 ,vol.64,no.4,pp.1623– Space Modulation with CSI: ran, " T IEEE Trans. Veh. Technol. ", function is MINIMIZED: g MSSK with Full-CSIT: Optimal Solution MSSK with Full-CSIT: Find Such that the followin Channel State Information at the Transmitter (9/22) Channel State Information at the Transmitter M.Maleki,H.R.Bahrami,S.Beygi,M.Kafashan,andN.H. Constellation Design and Performance1634, May Evaluation 2013. 143 ,vol.64,no.4,pp.1623– Space Modulation with CSI: ran, " T IEEE Trans. Veh. Technol. ", MSSK with Full-CSIT: Optimal Solution MSSK with Full-CSIT: : =2 t N If with Channel State Information at the Transmitter (10/22) Channel State Information at the Transmitter M.Maleki,H.R.Bahrami,S.Beygi,M.Kafashan,andN.H. Constellation Design and Performance1634, May Evaluation 2013. 144 f error =2is rs o t i the a p that r : s-th minimum MSSK with Full-CSIT: Optimal Solution MSSK with Full-CSIT: introduced is solution for N l , a sub-optimal iterative guarantee >2 stance ove t computed In eachvectorswiths-thminimum iteration,distance the is pairoptima considered of and the To performance does not increase with thefunction iterations, an error Iteration over s di transmission vectors N approach is proposed: If Channel State Information at the Transmitter (11/22) Channel State Information at the Transmitter 145 Channel State Information at the Transmitter (12/22) Channel State Information at the Transmitter 146 Channel State Information at the Transmitter (13/22) Channel State Information at the Transmitter 147 Channel State Information at the Transmitter (14/22) Channel State Information at the Transmitter 148 Channel State Information at the Transmitter (15/22) Channel State Information at the Transmitter 149 Channel State Information at the Transmitter (16/22) Channel State Information at the Transmitter 150 Link Adaptation for Spatial Modulation With Limited , vol. 61, no. 8, pp. 3808-3813, Oct. 2012. IEEE Trans. Veh. Technol. ", Channel State Information at the Transmitter (17/22) Channel State Information at the Transmitter P.Yang,Y.Xiao,L.Li,Q.Tang,Y.Yu,andS.Li," Feedback 151 Link Adaptation for Spatial Modulation With Limited max The Approach , vol. 61, no. 8, pp. 3808-3813, Oct. 2012. IEEE Trans. Veh. Technol. ", Channel State Information at the Transmitter (18/22) Channel State Information at the Transmitter P.Yang,Y.Xiao,L.Li,Q.Tang,Y.Yu,andS.Li," Feedback 152 Link Adaptation for Spatial Modulation With Limited , vol. 61, no. 8, pp. 3808-3813, Oct. 2012. The Proposed Adaptive Transmission Schemes Transmission TheProposed Adaptive : Adaptive Modulation Scheme Spatial Modulation : Optimal Hybrid Spatial Modulation : Concatenated Spatial Modulation IEEE Trans. Veh. Technol. : Adaptive Spatial Modulation ", AMS-SM ASM OH-SM C-SM Channel State Information at the Transmitter (19/22) Channel State Information at the Transmitter P.Yang,Y.Xiao,L.Li,Q.Tang,Y.Yu,andS.Li," Feedback 153 Channel State Information at the Transmitter (20/22) Channel State Information at the Transmitter 154 Channel State Information at the Transmitter (21/22) Channel State Information at the Transmitter 155 Channel State Information at the Transmitter (22/22) Channel State Information at the Transmitter 156 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. se 157 l mpu Il I l anne Ch l e h t s channel i b by d different channel knowledge to detect the mportant modulates the transmitted signal sconveye I i h on i a priori uc naturally M ormat f n ifi i ow e HMhIH i h t f art o p us, Response (CIR), i.e., the channel/spatial signature The wireless environment Each transmit-receive wireless link has a The receiver employs the Th transmitted signal Channel State InformationChannel State for SSK/SM Modulation? Imperfect Channel State Information (1/23) at the Receiver The working principle of SM/SSK1. is based on the following2. facts: 3. 4. 2 158 mi 2 E ,Vol.58,No. 2 State Information: l 2 mi E 2 1 00 mi Channe l NN E i 0 IEEE Trans. Commun. Ir ith Partia m ”, W Er ln tttststdt rtstdt mm TT n l Re ii iiii DD D i Space Shift Keying (SSK) Modulation i 1 t 1 t N i N i i i m m arg max ln arg max Perfect CSI (channel gains and phases): F–CSI (SSK) Partial CSI (channel gains): P–CSI (SSK) ˆ ˆ mD mD Imperfect Channel State Information (2/23) at the Receiver Optimal Detector and Performance Analysis Over Fading Channels 11, pp. 3196-3210, Nov. 2010. M. Di Renzo and H. Haas, “ 159 2 12 12 2 mi E 21 ii i 1 t N i i m ln arg max ln iimi 12 ˆ DDEr mD Nii 11 tt NN 121 iii 22 22 ,,Pr 2 212 112 112 212 2 00 00 g 00 00 mm mm NN NN mm m m NN NN lo ,E EE EE EE EE exp 12 ii tt hh NN ml l QQ QQ 22 2 11 1 m 22 2 11 1 T ABEP , APEP TX TX APEP E P 1, 2 rt E j wt nt rrtwtdt E Imperfect Channel State Information (3/23) at the Receiver P1,2 , , Pr 5 2 160 = = 2 2 =5 2 m , m 2, m 5 = = ,Vol.58,No. 1 1 =2, m 1 m , m State Information: 1, m 1 l = = 2 2 =1, m 2 Ω Ω Ω Ω Ω , Channe l 10, 10 =1, = = 1 1 1 Scenario a: Ω Scenario b: Ω Scenario c: Ω IEEE Trans. Commun. ith Partia ”, W =0.64) Nakagami-mFading ρ Space Shift Keying (SSK) Modulation 2x1 MIMO, Correlated2x1 MIMO, ( Imperfect Channel State Information (4/23) at the Receiver Optimal Detector and Performance Analysis Over Fading Channels 11, pp. 3196-3210, Nov. 2010. M. Di Renzo and H. Haas, “ 161 4 - 4i = 4 ,Vol.58,No. ,…, State Information: i=2 4 |i-j|) 0 l } i = 1 Ω Ω { 1, { Channe i=1,…,4 l } = i =exp(-d = 0.22 1 ,j 0 i Ω Balanced: { Unbalanced: Ω Correlation: ρ d IEEE Trans. Commun. ith Partia ”, W Space Shift Keying (SSK) Modulation 4x1 MIMO, Correlated4x1 MIMO, (exponential) Nakagami-m Fading Imperfect Channel State Information (5/23) at the Receiver Optimal Detector and Performance Analysis Over Fading Channels 11, pp. 3196-3210, Nov. 2010. M. Di Renzo and H. Haas, “ 162 CSI - P F-CSI ,Vol.58,No. State Information: l …… ____ Channe l IEEE Trans. Commun. ith Partia ”, W Space Shift Keying (SSK) Modulation 2x1 MIMO, Uncorrelated2x1 MIMO, Nakagami-m Fading Imperfect Channel State Information (6/23) at the Receiver Optimal Detector and Performance Analysis Over Fading Channels 11, pp. 3196-3210, Nov. 2010. M. Di Renzo and H. Haas, “ 163 CSI - P F-CSI ,Vol.58,No. State Information: l …… ____ Channe l IEEE Trans. Commun. ith Partia ”, W Space Shift Keying (SSK) Modulation 4x1 MIMO, Correlated4x1 MIMO, (exponential) Nakagami-m Fading Imperfect Channel State Information (7/23) at the Receiver Optimal Detector and Performance Analysis Over Fading Channels 11, pp. 3196-3210, Nov. 2010. M. Di Renzo and H. Haas, “ 164 2 p 0 p N EN 0, Estimated Antenna Index N ,, , tr qr tr Space Shift Keying (SSK-) MIMO with Practical 00 mm NN EE ˆ 0 ML r , Vol. 60, No. 4, pp. 998-1112, Apr. 2012. , SSK 0 N ML-Detector q 1 r mt N ˆ r Channel Estimator SSK with Mismatched Decoder min g IEEE Trans. Commun. for 1,2, , for 1,2, , arg min ar ”, tt tt mt N mt N ˆ mDm Signal Received Received Imperfect Channel State Information (8/23) at the Receiver Channel Estimates M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “ 165 2 1 r N r α ,,, tq qr tr Space Shift Keying (SSK-) MIMO with Practical statistics) l ABEP , Vol. 60, No. 4, pp. 998-1112, Apr. 2012. qq qq ˆˆ ˆˆ , tq IEEE Trans. Commun. Eexp q t mt mq mt mq ”, m m Dm Dm Dm Dm q , APEP E Pr Pr 0 t Union bound: the ABEP can be obtained from the APEP The (difference) decision(when variable conditioning is upon fading a channe quadratic-formThe PEP in is complex obtained by Gaussian using RVs the Gil-Pelaez inversion theorem Ms s Imperfect Channel State Information (9/23) at the Receiver Methodology for computation: 1. 2. 3. Channel Estimates M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “ 166 ) . ) () ( (.) 1 2 w w ,vol.59,no.1,pp.116- TOSD-SSK ( r no mod. andwithasingleactive 0 1 IEEE Trans. Commun. ”, Diversity = 2N ) MIMO over Correlated Rician Fading Channels: – K (t) 2 (conventional SSK) r 0 ”tow time-instance with no bandwidth expansion y t Space Space Shift Keying (SS AI-2 = Diversity = N Shift Keying at an (t) 2 time-orthogonal (t) = w (t) is “ Time-Orthogonal Signal Design assisted SSK (TOSD-SSK) 1 1 AI-1 If w If w This is true fortransmit-antenna any N Imperfect Channel State Information (10/23) at the Receiver AI-2 M.DiRenzoandH.Haas,“ Performance Analysis and a New Method for Transmit–Diversity 129, Jan. 2011. 167 2 p r 0 p N m 2 EN E 0, Estimated Antenna Index N Space Shift Keying (SSK-) MIMO with Practical ˆ EE Estimator ˆˆ ˆ ML , Vol. 60, No. 4, pp. 998-1112, Apr. 2012. ,, , , , , qr tr m tq tr m tr tr TOSD-SSK TOSD-SSK ML-Detector q 11 r mt Channel Estimator NN ˆ rr TOSD-SSK with Mismatched Decoder TOSD-SSK IEEE Trans. Commun. ”, for 1,2, , for 1,2, , arg min arg min Re tt tt mt N mt N ˆ Signal mDm Received Received Imperfect Channel State Information (11/23) at the Receiver Channel Estimates M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “ ) 168 cs i 2 st iii ) quadratic- stat l anne hl h 2 ng c di a fdi f 11 rr NN rr ng upon i on i t Space Shift Keying (SSK-) MIMO with Practical di i i difference of two independent ,,, tq q qr t tr en con ABEP h w (h ( , Vol. 60, No. 4, pp. 998-1112, Apr. 2012. s qq qq ˆˆ ˆˆ RV an i , auss tq Gi G ex l IEEE Trans. Commun. q t mt mq mt mq ”, m m Dm Dm Dm Dm Eexp ncomp i APEP E Pr Pr 0 orms Union bound: the ABEP can be obtained from the APEP The (difference) decisionf variable is the The PEP is obtained by using the Gil-Pelaez inversion theorem , tq Imperfect Channel State Information (12/23) at the Receiver Ms s Channel Estimates M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “ Methodology for computation: 1. 2. 3. 169 Space Shift Keying (SSK-) MIMO with Practical SSK OSD-SSK T , Vol. 60, No. 4, pp. 998-1112, Apr. 2012. Diversity Analysis (i.i.d. RayleighDiversity Fading) IEEE Trans. Commun. ”, order of SSK is: Nr y Diversit Diversity order of TOSD-SSK is: 2Nr Imperfect Channel State Information (13/23) at the Receiver Channel Estimates M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “ With channel estimation errors: 1. 2. 170 Numerical Results (SSK) Numerical Results Imperfect Channel State Information (14/23) at the Receiver 171 Numerical Results (TOSD-SSK) Numerical Results Imperfect Channel State Information (15/23) at the Receiver 172 Single-Antenna MQAM Single-Antenna Imperfect Channel State Information (16/23) at the Receiver 173 Alamouti MQAM Imperfect Channel State Information (17/23) at the Receiver 174 e: g Messa y yg SSK vs. Single-Antenna MQAM (Nr=1 / Nr=2 Nr=4) MQAM SSK vs. Single-Antenna ake Awa ake SSK is better than single-antenna MQAM if is better MQAM than single-antenna SSK Rate>2bpcu and Nr>1 The robustness to channel estimation errors is the same T • • Imperfect Channel State Information (18/23) at the Receiver 175 TOSD-SSK vs. Alamouti MQAM (Nr=1 / Nr=2) vs. Alamouti MQAM TOSD-SSK OSD-SSK is better than Alamouti MQAM ifis better OSD-SSK than Alamouti MQAM Rate>2bpcu T TOSD-SSK is more robustTOSD-SSK to channel estimation errors Take Away Message: Away Take • • Imperfect Channel State Information (19/23) at the Receiver 176 2 2 s , s , 2 rjr y rjr 2 y 1 r N 1 r r N r , min 11 js N , g js ar , ˆ s , ,g ˆ s j jy ˆ ,argmin j ˆ , vol. 16, no. 2, pp. 176-179, Feb. 2012. Performance of Spatial Modulation in the Presence of with 1 1 SM with Imperfect CSIR const and 1 IEEE Commun. Lett. 22 ”, AM modulation: Q Channel estimation model: SM with MPSK modulation: SM with M Imperfect Channel State Information (20/23) at the Receiver Channel Estimation Errors E.Basar,U.Aygolu,E.Panayirci,andV.Poor,“ 177 4 =4 = r N , vol. 16, no. 2, pp. 176-179, Feb. 2012. Performance of Spatial Modulation in the Presence of IEEE Commun. Lett. ”, Imperfect Channel State Information (21/23) at the Receiver Channel Estimation Errors E.Basar,U.Aygolu,E.Panayirci,andV.Poor,“ 178 4 =4 = r N , vol. 16, no. 2, pp. 176-179, Feb. 2012. Performance of Spatial Modulation in the Presence of IEEE Commun. Lett. ”, Imperfect Channel State Information (22/23) at the Receiver Channel Estimation Errors E.Basar,U.Aygolu,E.Panayirci,andV.Poor,“ 179 4 =4 = r N , vol. 16, no. 2, pp. 176-179, Feb. 2012. Performance of Spatial Modulation in the Presence of IEEE Commun. Lett. ”, Imperfect Channel State Information (23/23) at the Receiver Channel Estimation Errors E.Basar,U.Aygolu,E.Panayirci,andV.Poor,“ 180 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 181 facts: for Response g used Impulse be Channel channel the channel by different is based on the followin fading K channel knowledge to detect the transmitted conveyed modulates the transmitted signal SS the / is of apriori naturally le of SM p information the rinci p pp of randomness g part the Multiple-Access too rather than just forModulation? Multiple-Access too signal The wireless environment Each transmit-receive wireless link has a The receiver employs the Thus, (CIR), i.e., the channel/spatial signature Can the randomness of the fading channel be used for he workin Multiple Access Interference (1/22) . T 1. 2. 3. 4 182 , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. IEEE Trans. Veh. Technol. Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ”, Detector User - Multiple Access Interference (2/22) Signal Model Single Multi-User Detector M. Di Renzo and H. Haas, “ Independent Fading Channels 183 uu r 1 N u N u t 1 r N r , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. Nr r r N N 1 r 21 N r 1 r r Nr \ N 0\ 0 << 1 (noise limited): ξ \ 22 IEEE Trans. Veh. Technol. ABEP 2 SNR Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ”, EN EN SN 1SINR 1SINR SSK with Single-User Detector (i.i.d. Rayleigh)with Single-User SSK t 2 2 2 SINR 2 2 SINR N SINR SNRSNR 1 INR and INR >> 1 and INR ξ = 0 (no interference): framework reduces to single-user case u E SNR Multiple Access Interference (3/22) ABEP 1 1 M. Di Renzo and H. Haas, “ Independent Fading Channels 184 1 u N u uu 22 1 u N u , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. >> 1 (interference limited): ξ \ r N tr \ tuu NQN /INR NEE ξ 0\ 0 r r N 22 N 21 EN EN IEEE Trans. Veh. Technol. 1 Bit Error Probability of Space Shift Keying MIMO over Multiple-Access r ”, ABEP 2 SINR N SINR SNRSNR 1 INR and INR SSK with Single-User Detector (i.i.d. Rayleigh)with Single-User SSK >> 1 and SIR = SNR ABEP 2 SIR with SIR ξ \ >> 1: r N INR Multiple Access Interference (4/22) M. Di Renzo and H. Haas, “ Independent Fading Channels 185 r N 2 Q 2. This occurs if ≥ ,2 > 4 a crossing point exists xy y x t , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. H Ns s s s (same bpcu) t 11 tt NN xy =4.IfM=N M = N t 2 2 IEEE Trans. Veh. Technol. Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ”, tt NN =2andM=N PSK SSK 2 t SSK vs. MPSK/MQAM (Single-User Detector, i.i.d. Rayleigh) Detector, (Single-User MPSK/MQAM vs. SSK ABEP log ABEP r M=N SSK will never be better than MPSK/MQAM if Q If Q Multiple Access Interference (5/22) M. Di Renzo and H. Haas, “ Independent Fading Channels 186 ta d r 2N N ≤ limite ≠ ta N r r ≤ N N 21 y , r N x N ta y , N , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. x \ta SNR ta ta antennas NN 21 INR 1 : limited or SIR interference active IEEE Trans. Veh. Technol. of Bit Error Probability of Space Shift Keying MIMO over Multiple-Access y ”, SINR performance x number GSSK with Single-User Detector (i.i.d. Rayleigh)with Single-User GSSK SNR noise noise limited or SIR interference limite the is the number of different antenna indexes: 2 APEP is ≠ ta ta N N Asymptotic Multiple Access Interference (6/22) M. Di Renzo and H. Haas, “ Independent Fading Channels 187 r N SSK o t , ta d N xy 2 ta N N , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. 10 ta compare diagram GSSK SSK xy , GSSK is worse than SSK regardless of the ta eworse 010log constellation - th 2N , GSSK IEEE Trans. Veh. Technol. APEP Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ta ”, ≤ N ≠ spatial ta APEP N the arger l ≤ e SSK vs. GSSK (Single-User Detector, i.i.d. Rayleigh) SSK vs. GSSK (Single-User Detector, of th us, Since 2 choice The SNR gap is: th Multiple Access Interference (7/22) M. Di Renzo and H. Haas, “ Independent Fading Channels 188 0 → 0 , uu xy 1 0ifN r r → Nr , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. , , 1 r uu uu N r r Nr 0 ta N 1 Nxy 2 2 0ta NN uu xy uu 2 E E 1 1 u u N N u u IEEE Trans. Veh. Technol. Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ”, rSNR 1 2 2 AggrSNR 2 2 AggrSNR 1 AggrSNR 1 AggrSNR gg AggrSNR A SSK and GSSK with Multi-User Detector (i.i.d. Rayleigh) xy SSK GSSK Unlike the single-user detector, APEP Multiple Access Interference (8/22) APEP 1 1 M. Di Renzo and H. Haas, “ Independent Fading Channels r 2 189 , uu rSNR gg 2 y NxyE uu xy E , 1 u xy H N r u N 21 1, , t xy N r r , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. 2 N N g 21 lo xy 1 ) r 1 u N NN 1 tt r N NN 21 ,A = u r (N r N N 21 SULB 1 IEEE Trans. Veh. Technol. Bit Error Probability of Space Shift Keying MIMO over Multiple-Access bound ”, ABEP 2 SNR SULB 1 2 lower g ggg ABEP ABEP >> u N tt xyH user - NN Nx log log SNR rr NN 10 10 1 gg ASNR A Single SNR gap due to multiple-access interference SSK with Multi-User Detector (i.i.d. Rayleigh)SSK – Analysis Asymptotic SNR 10 10 Multiple Access Interference (9/22) M. Di Renzo and H. Haas, “ Independent Fading Channels ABEP lo 190 ) u ur t r y N 0 r N or ever ,foreveryu) f 2SULB 2 , u 0 2 σ u u , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. σ u SULB 2 E << E 2 >> r w r 2 N σ b N w σ b 21 bb r (E r N NEN N 1 21 IEEE Trans. Veh. Technol. r Bit Error Probability of Space Shift Keying MIMO over Multiple-Access N u ”, N NEN 1 erence case r r f N wt www NNNN nter if i 10 log ABEP ABEP 10 1 log k ABEP 2 ABEP bt bb ea b Strong interference case (E Wk W SSK with Multi-User Detector (i.i.d. Rayleigh)SSK – Analysis Asymptotic SNR 10 10 Multiple Access Interference (10/22) M. Di Renzo and H. Haas, “ Independent Fading Channels ABEP 2 191 g r N r N , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. 2 0 2 uu 0 N E uu N E UL r r r N r N N 21 N ABEP ABEP 10 1 lo 21 u N IEEE Trans. Veh. Technol. Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ”, N N g gg 1 1 r r N N ruuurt NNNN 10 lo L U ut ut LU uuu u Generic user SNR 10 10 ABEP 2 ABEP 2 SSK with Multi-User Detector (i.i.d. Rayleigh)SSK – Analysis Asymptotic ABEP ABEP ABEP Multiple Access Interference (11/22) M. Di Renzo and H. Haas, “ Independent Fading Channels r N 192 2 0 uu r N 2 r r 0 uu UU u r N ta ABEP N r r t ta t N ta N N N 2 2 r log log N ta u u N N 1 t ta N r N N 2 , Vol. 60, No. 8, pp. 3694- 3711, Oct. 2011. log UU LL U u r .i.d. Rayleigh) – Analysis Asymptotic ABEP 2 2 L tr t r NN N N ruu 10 10 2 N 0 uu NN r EE 10 log ABEP ABEP N IEEE Trans. Veh. Technol. 10log 10 log 2 Bit Error Probability of Space Shift Keying MIMO over Multiple-Access ”, 2 u 0 r uu NN r SNR 10 NN EE NN 21 21 LL u t r ta r N N NN ABEP NN 2 g 21 21 lo u tu N NN LU LU uu uuu 1 11 r r N NN GSSK with Multi-User Detector (i ABEPABEP SULB and ABEP ABEP ABEP weak interference case 22 Multiple Access Interference (12/22) 2ABEP22 M. Di Renzo and H. Haas, “ Independent Fading Channels 193 Multiple Access Interference (13/22) 194 Multiple Access Interference (14/22) 195 Multiple Access Interference (15/22) 196 Multiple Access Interference (16/22) 197 Multiple Access Interference (17/22) 198 Multiple Access Interference (18/22) 199 Multiple Access Interference (19/22) 200 Multiple Access Interference (20/22) 201 Multiple Access Interference (21/22) ”, 202 shown is 3-user scenario The ABEP of each user is shown Multiple Access Spatial Modulation , September 2012. Multiple Access Interference (22/22) EURASIP Journal on Wireless Communications and Networking N.Serafimovski,S.Sinanovic,M.DiRenzo,andH.Haas,“ 203 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 204 ower p on i ss ii i ower consumed p l east transm l e h An Energy Saving Base Station Employing the tota natt i a hi h c RF antenna of a BS to er p per on i ower p , Sep. 2012, Barcelona, Spain. power - transmit ower consumpt IEEE CAMAD p transmitted RF e ”, is the total power supplied to the BS h is the maximum transmit-power per antenna the is the number of RF chains at the BS st i is the slope of the load-depended power consumption is RF supply 0 t max G. Auer etMay 2011 al., “Cellular Energy Evaluation Framework,” IEEE VTC-Spring, P N P m P P The EARTH powerrelates model the is a very simple and elegant model that Energy Efficiency (1/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ 205 An Energy Saving Base Station Employing , Sep. 2012, Barcelona, Spain. IEEE CAMAD ”, Energy Efficiency (2/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ 206 2 2 m h 0 2 2 m h 2 2 1 t h N m g1 0 t An Energy Saving Base Station Employing o P N l1 l P 0 1 t N NN m t NN WP 1 g lo , Sep. 2012, Barcelona, Spain. supply P CSIT log 1 IEEE CAMAD Capacity ”, MISO 2 SM 1STBC 2 1 STBC 2 2 EE CWR CCCC CW Energy Efficiency (3/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ 207 An Energy Saving Base Station Employing , Sep. 2012, Barcelona, Spain. IEEE CAMAD ”, Energy Efficiency (4/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ 208 An Energy Saving Base Station Employing , Sep. 2012, Barcelona, Spain. IEEE CAMAD ”, Energy Efficiency (5/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ 209 An Energy Saving Base Station Employing , Sep. 2012, Barcelona, Spain. IEEE CAMAD ”, Energy Efficiency (6/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ 210 An Energy Saving Base Station Employing , Sep. 2012, Barcelona, Spain. IEEE CAMAD ”, Energy Efficiency (7/26) Spatial Modulation A. Stavridis, S. Sinanovic, M. Di Renzo, H. Haas, and P. Grant, “ y 211 and nerg E d MIMO of ormance an f er Pf P e h efficiency st - d owar Td T wavelength “ Energy “ s, ) the ki is the bit rate is the link margin l is b ou λ R ik M PAPR er ( () Bahai, Vikki V . . freqSynt C A d , Sep. 2012, Barcelona, Spain. +P and ra, an ower-ratio filters i e p Ni N e- +P g erez- P IEEE CAMAD mixer . ”, figure Goldsmith, A +P . J . are transmit and receive antenna gains DAC 1098, Aug. 2004 enzo, noise r eak-to-avera A R − p G =P the Di . is Cui, is the bit energy and f is the transmission distance M is the drain efficiency of the power amplifier t b is the circuit . S cooperative MIMO techniques inpp. sensor 1089 networks”, IEEE JSAC, vol. 22, no. 6, E d G N η ξ P n, i tont The following energy-model is considered: Ni N . Energy Efficiency (8/26) K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over y 212 nerg E d ormance an f er Pf P e h st d rate owar bit Td T “ the s, ki is is the link margin l is the wavelength b ou λ R ik M er (PAPR) Vikki V o . i freqSynt C d , Sep. 2012, Barcelona, Spain. +P ra, an ower-rat filters i e p Ni N +P erez- P IEEE CAMAD mixer . ”, A +P energy -to-average- k are transmit and receive antenna gains DAC enzo, bit r ea R p G =P e the Di h . is the noise figure is and st f is the transmission distance M is the drain efficiency of the power amplifier t b i circuit E d G N η ξ P n, i tont The following energy-model is considered: Ni N . Energy Efficiency (9/26) K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over y 213 nerg E d ormance an f er Pf P e h st d owar Td T “ s, ki ou ik er Vikki V . C d , Sep. 2012, Barcelona, Spain. ra, an i e Ni N erez- SM vs. Single-RF QAM – QAM SM vs. Single-RF bpcu 4 P IEEE CAMAD . ”, A enzo, R Di . M n, i tont Ni N Energy Efficiency (10/26) . K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over y 214 nerg E d ormance an f er Pf P e h st d owar Td T “ s, ki ou ik er Vikki V . C d , Sep. 2012, Barcelona, Spain. ra, an i e Ni N erez- SM vs. Single-RF QAM – QAM SM vs. Single-RF bpcu 4 P IEEE CAMAD . ”, A enzo, R Di . M n, i tont Ni N Energy Efficiency (11/26) . K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over y 215 nerg E d ormance an f er Pf P e h st d owar Td T “ s, ki ou ik er Vikki V . C d , Sep. 2012, Barcelona, Spain. ra, an i e Ni N erez- SM vs. Single-RF QAM – QAM SM vs. Single-RF bpcu 4 P IEEE CAMAD . ”, A enzo, R Di . M n, i tont Ni N Energy Efficiency (12/26) . K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over y 216 nerg E d ormance an f er Pf P e h st d owar Td T “ s, ki ou ik er Vikki V . C d , Sep. 2012, Barcelona, Spain. ra, an i e Ni N erez- SM vs. Single-RF QAM – QAM SM vs. Single-RF bpcu 4 P IEEE CAMAD . ”, A enzo, R Di . M n, i tont Ni N Energy Efficiency (13/26) . K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over y 217 nerg E d ormance an f er Pf P e h st d owar Td T “ s, ki ou ik er Vikki V . C d , Sep. 2012, Barcelona, Spain. ra, an i e Ni N erez- SM vs. Single-RF QAM – QAM SM vs. Single-RF bpcu 4 P IEEE CAMAD . ”, A enzo, R Di . M n, i tont Ni N Energy Efficiency (14/26) . K Efficiency ComparisonGeneralized of Fading Channels Spatial Modulation with Conventional Single-Antenna Transmission over l y 218 where uentl on q qy ti a l u dlti d mo HSSK) - (EE d e mbols are used more fre y non-equiprobable signaling Aid d , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. e- d o Energy Efficient Transmission over Space Shift Keying Cd C ng i modulation s , where minimum achievable average symbo g amm IEEE Trans. Commun. Hi H t ", roblem p en i c Effi i t - y ower-consumin energy efficient modulation design is formulated as a convex p nerg less Energy efficiency is achieved by to transmit a given amount ofThe information optimization power consumption is derivedconstraints with rate, performance, and hardware E Energy Efficiency (15/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 219 diagram , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. Energy Efficient Transmission over Space Shift Keying =2) From GSSK … GSSK From constellation min - : spatial IEEE Trans. Commun. ", the GSSK of of Transmission rate Selection System performance (d Limitations Energy Efficiency (16/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 220 of RF binary r utilized fully general, of 1’s in each is r (in chains Theindices set ofIt employsnumbe antenna amodulation different based on thecode Hamming symbol linearconstruction technique blockIncreased numbe code) In HSSK: , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. Energy Efficient Transmission over Space Shift Keying … to (EE)-HSSK IEEE Trans. Commun. ", Energy Efficiency (17/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 221 rate M ≤ transmission transmitter the target design an alphabet and at minimum average symbol the chains while RF i , so that , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. constraint) are met of RF chains is restricted to i r Energy Efficient Transmission over Space Shift Keying requires consumes power equal to i achieved i i constraint), and the maximum required C is hardware in Problem Formulation constraint), the minimum Hamming distance } with the specified minimum distance property i IEEE Trans. Commun. ", element performance transmission each per that he objective of EE-HSSK modulation is to spectral-efficiency property ( the symbol a priori probabilities T power ( number of RF chains ( Given a code C = {C Given Given that each element in C Given that the maximum numbe Then… Energy Efficiency (18/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 222 et information g symbols in the formula l =0ifi>M he tar i The a prioriof probabilities al T rateofmbitsismet,as described byentropy Shannon’s alphabet sum toP one, and formulated as: y , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. Energy Efficient Transmission over Space Shift Keying Problem Formulation IEEE Trans. Commun. ", roblem is mathematicall p … the design Energy Efficiency (19/27) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 223 using found be associated to the i can P which , robabilities p , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. solution can be computed as follows: Energy Efficient Transmission over Space Shift Keying 2 λ Optimal Solution optimal and 1 λ roblem has a linear objective function subject to p IEEE Trans. Commun. riori transmission ", p globally a a l with tima p he optimization he o T convex the Lagrange multiplier method T Lagrange multipliers an affine equality and convex inequality constraints. Therefore, it is Energy Efficiency (20/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " no 224 et: b in a a largest h y p lhb l the ea uentl However, h q qy t . r o have f to es i less fre t is coding r bili a ea -consuming are used less probabilities r bbilii b cost pp ro p owe i sa p priori The Huffman g or : ii i a . r p a rate l , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. bit strin symbol ma il i r Energy Efficient Transmission over Space Shift Keying e y g probabilities eopt h Optimal Solution optimal information priori nes t the a i IEEE Trans. Commun. .Sincelon r ", highest eterm di d owe unequal , only the least power-consuming codewords in C are included provides β p the + , all codewords in C are included in the alphabet equiprobably to f =0 with =1 ue o β β l mbol y solution frequently to achieve energy efficiency If achieve average symbol power consumption If in the alphabet equiprobabl The length ofs the bitrandom strings input is sequence, symbols roughly more reversely proportional to the eva Th The information iscoding given for is accomplishingsymbols proposed the for bit creating mapping. an Variable-length efficient bit-string representation of Energy Efficiency (21/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 225 , vol. 60, no. 10, pp. 2950-2959, Oct. 2012. Energy Efficient Transmission over Space Shift Keying Implementation IEEE Trans. Commun. ", Energy Efficiency (22/26) Modulated MIMO Channels R. Y. Chang, S.-J. Lin, and W.-H. Chung, " 226 7 = t N Energy Efficiency (23/26) 227 = 10 t N Energy Efficiency (24/26) 228 7 = r N = t N (Single RF-SIMO) (Single Energy Efficiency (25/26) 229 = 10 r = N t N (Single RF-SIMO) (Single ( Two-RF-MIMO) Energy Efficiency (26/26) 230 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 231 IEEE J. Sel. Areas ”, S2 S1 * * S1 -S2 Block - Time - Coding Orthogonal The Alamouti Scheme [dB] Space SNR [dB] S1 A simple transmit diversity technique for wireless communications EP S S A S2 , vol. 16, no. 8, pp. 1451–1458, Oct. 1998. Transmit-Diversity for SM (1/61) Transmit-Diversity S. M. Alamouti, “ Commun. 232 , vol. 17, no. 3, pp. 451–460, Mar. 1999. Space–time block coding for wireless communications: IEEE J. Sel. Areas Commun. ”, Orthogonal Space-Time Block Codes (OSTBCs) Transmit-Diversity for SM (2/61) Transmit-Diversity V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “ Performance results 233 S1 S1 S2 S2 * * * * S2 S1 S1 -S2 - STBC STBC Alamouti Alamouti ith rate greater than one and single-stream w 0 1 y OpportunitiesChallenges for and SM Transmit-diversity with rate greater than one ransmit-diversity T S1 S2 Opportunity: Challenge: decoding complexit AI Transmit-Diversity for SM (3/61) Transmit-Diversity 234 exp exp 2 1 2 1 exp exp 11 11 22 nn 22 2 0 11 1 0 22 nn htht j j t t nm n nm n mm mm TT TT 2222 1111 stmstm E E j stm j stm Re Re 2222 1111 D rts tdtD s ts tdt rts tdt s ts tdt , pp. 1–6, May 2010. 2122 2 2 11121 1 Performance comparison of different spatial modulation schemes in correlated rtm s tmrtm s tm s tm nt s s t tm nt nt s t nt if if 221 112 IEEE Int. Conf. Commun. mDD mDD ”, ˆ m Transmit-Diversity for SM (4/61) Transmit-Diversity SSK SS M. Di Renzo and H. Haas, “ fading channels , 235 power 4 no m m nt EN 2 2 20 IEEE Veh. Technol. Conf. – Fall radiates ”, j 1 2 TX 11 are both active and 2 E nt active and TX is j 1 1 j 0 p TX : exp exp ex mm m 2 2 21 1112 2 2 stm stm stm stm 020 m NEN 21122 transmitted 111 E 42214 be rtm E rtm E to Space Modulation on Wireless Fading Channels Q needs to be transmitted: TX needs BEP ABEP 1 2 m m If If Transmitted Signal: Received Signal: Error Probability: Transmit-Diversity for SM (5/61) Transmit-Diversity Y. Chau and S.-H. Yu, “ vol. 3, pp. 1668-1671, Oct. 2001. 236 , pp. 1–6, May 2010. Performance comparison of different spatial modulation schemes in correlated IEEE Int. Conf. Commun. ”, Transmit-Diversity for SM (6/61) Transmit-Diversity M. Di Renzo and H. Haas, “ fading channels 0 237 0 IEEE ”, 4 m m EN EN 2 2 power 2 is active 2 no 221 4 11 12 12 radiates 2 222 ABEP nt nt TX 0 1 and j j p exp ex active radiates no power and TX is 2 m 1 m 1 Space Shift Keying Modulation for MIMO Channels TX : 112 2 1221 , vol. 8, no. 7, pp. 3692-3703, July 2009. 222 111 stm stm stm stm rtm E rtm E transmitted jj be to exp exp 2211 0 m needs to be transmitted: TX needs N E 1 2 4 m m If If Q Transmitted Signal: Received Signal: Error Probability: Transmit-Diversity for SM (7/61) Transmit-Diversity BEP J.Jeganathan,A.Ghrayeb,andL.Szczecinski,“ Transactions on Wireless Communications 238 , pp. 1–6, May 2010. Performance comparison of different spatial modulation schemes in correlated IEEE Int. Conf. Commun. ”, Transmit-Diversity for SM (8/61) Transmit-Diversity M. Di Renzo and H. Haas, “ fading channels 239 1 power is active 2 no 0 2 4 m EN radiates 2sin 2 22 2222 12 12 Md TX 2 0 wt nt wt nt and 1 12 41 j j active exp radiates no power and TX is exp Ms s s ABEP 1 1 and 0 m ): m TX : 0 2222 1111 m t TOSD-SSK rtm E rtm E transmitted be : 22 : 12 to ms tm wt wt wt dt tt 0 m 22 2 1 122 11 1 1221 N stm wt s st E Signal 4 needs to be transmitted: TX needs 1 2 m m Probability Q If If Transmitted Signal ( Received Error BEP Transmit-Diversity for SM (9/61) Transmit-Diversity 240 ) . (.) ( () 1 2 ) w w no mod. TOSD-SSK 0 1 SSK) Diversity=2( entional v (t) 2 (con entional 1 , pp. 1–6, May 2010. = ”tow ace ersity Sp v AI-2 = 0 Di ersit Shift Keying Performance comparison of different spatial modulation schemes in correlated (t) 2 time-orthogonal w IEEE Int. Conf. Commun. ”, = (t) (t) is “ 1 1 AI-1 w If If w Transmit-Diversity for SM (10/61) Transmit-Diversity AI-2 M. Di Renzo and H. Haas, “ fading channels 241 , pp. 1–6, May 2010. Performance comparison of different spatial modulation schemes in correlated IEEE Int. Conf. Commun. ”, Transmit-Diversity for SM (11/61) Transmit-Diversity M. Di Renzo and H. Haas, “ fading channels 242 me- Ti u Y d : SM - :Meslehetal. au an n assisted SM ] g 5 [ Ch - ] ] 1 3 OrthogonalDesi Signal [ [ and Jeganathan et al. TOSD , pp. 1–6, May 2010. Performance comparison of different spatial modulation schemes in correlated IEEE Int. Conf. Commun. ”, Transmit-Diversity for SM (12/61) Transmit-Diversity M. Di Renzo and H. Haas, “ fading channels 243 ) ) . . ) () ( () ( 1 2 ,vol.59,no.1, w w > 1 r TOSD-SSK ( no mod. r andwithasingleactive rrelated Rician Fading Channels: 0 1 IEEE Trans. Commun. > 2, and N t ”, Diversity = 2N (t) j (conventional SSK) r ”tow time-instance with no bandwidth expansion y t Space AI-2 = 0 Space Shift Keying (SSK–) MIMO over Co Shift Keying Diversity = N at an (t) j time-orthogonal Generalization to Rician Fading, N Rician Fading, Generalization to (t) = w (t) is “ i i AI-1 If w If w This is true fortransmit-antenna any N Transmit-Diversity for SM (13/61) Transmit-Diversity AI-2 Performance Analysis andpp. a 116-129, New Jan. 2011. Method for Transmit–Diversity M. Di Renzo and H. Haas, “ 244 pr. 2012. A p. 998-1112, p Space Shift Keying (SSK-) MIMO with Practical ol. 60, No. 4, Spectrally efficient UWB pulse shaping with application in V , , vol. 55, no. 2, pp. 313–322, Feb. 2007. rans. Commun. T IEEE ”, IEEE Trans. Commun. ,” Orthogonal Waveforms Design with Bandwidth Constraint Orthogonal Waveforms Estimates l Transmit-Diversity for SM (14/61) Transmit-Diversity J.A.NeydaSilvaandM.L.R.deCampos,“ orthogonal PSM M. Di Renzo, D. DeChanne Leonardis, F. Graziosi, and H. Haas, “ 245 Space Shift Keying (SSK-) MIMO with Practical , Vol. 60, No. 4, pp. 998-1112, Apr. 2012. IEEE Trans. Commun. ”, Transmit-Diversity for SM (15/61) Transmit-Diversity Channel Estimates M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “ 246 Transmit-Diversity for SM (16/61) Transmit-Diversity 247 Transmit-Diversity for SM (17/61) Transmit-Diversity 248 Transmit-Diversity for SM (18/61) Transmit-Diversity 249 Transmit-Diversity for SM (19/61) Transmit-Diversity 250 Transmit-Diversity for SM (20/61) Transmit-Diversity 251 the Full it? at This is too antenna achieving ual to 2 ) t q e ? (N 2 y 2 active onl scheme 1 than y just SSK a greater , June 2011. with =2 t design gain to diversity ons - ti how diversity ca - li ti achieves transmit-diversit Space Shift Keying (SSK) Modulation: On the Transmit- transmit eapp IEEE Int. Commun. Conf. K t diversity), ”, transmit a ara t a (rate, dt d achieves OSD-SS h T g , pair achieve hi h SSK - a r o we f ow TOSD transmitter However transmit-diversity is possible only if N Furthermore, the datal rate of SSK is only Rate=log Can At the same time, can weGiven increase the rate? In summary: Questions: Transmit-Diversity for SM (21/61) Transmit-Diversity Diversity/Multiplexing Trade-Off M. Di Renzo and H. Haas, “ 252 ) t t a N N (N 2 2 log D={1; 2; 3; 4; 5} 2 )>log H H N Generalized space shift keying modulation for MIMO (N D={(1,2); (1,3); (1,4); (1,5); (2,3); (2,4);D={(1,2,3); …} (1,2,4); (1,2,5); (1,3,4); …} 2 ) t =1 (i.e., SSK) =2 =3 a a a >N N N N H Increasing the Rate via GSSK Size(N of the spatial-constellation diagram Rate = log Spatial-constellation diagram: , pp. 1-5, Sep. 2008. t N 1 2 3 4 5 IEEE PIMRC a TX TX TX TX TX ”, N Transmit-Diversity for SM (22/61) Transmit-Diversity J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “ channels e h 253 t d yze such that ld l antennas - H ~ N ≤ H ave ana h transmit d active the be , June 2011. agram an t a a di N N N 2 log on i and 2 at ll i H N antennas -conste l - Space Shift Keying (SSK) Modulation: On the Transmit- a il i IEEE Int. Commun. Conf. ”, is Div y transmit espat h the nt i be t N nts i o transmit-diversit transmit-diversity order of each of them Let Then, the largest possible size of the spatial-constellation diagram is: Find the actual spatial constellation diagram ofUnderstand size the N role played by the TOSD principle for transmit-diversity We have computed thep PEP (Pairwise Error Probability) of any pair of Problem statement Objectives Methodology Transmit-Diversity for SM (23/61) Transmit-Diversity Diversity/Multiplexing Trade-Off M. Di Renzo and H. Haas, “ if 254 ) H =1 if the a ) (N H 2 if N (N 2 K R=log 1 = rate a OSD-SS T N and if 2 SSK Div= to , June 2011. Div=1 and rate R=log y and reduces to K reduces diversity - and OSD-GSS transmit GSSK T Space Shift Keying (SSK) Modulation: On the Transmit- IEEE Int. Commun. Conf. ”, called is achieves Main Result: Transmit-Diversity 1 and 2 Transmit-Diversity Main Result: transmit-antennas have orthogonal shaping filters t scheme system transmit-antennas have the same shaping filter t he system achieves transmit-diversit his scheme is called N T This The the N T Result 1 (Div=1) Result 2 (Div=2) Transmit-Diversity for SM (24/61) Transmit-Diversity Diversity/Multiplexing Trade-Off M. Di Renzo and H. Haas, “ 255 filters distinct a and rate a N shaping · transmit-antennas t a Div=2 N orthogonal have , June 2011. tradeoff antennas - artition of the set of N t a p each subset of the partition has N N N transmit 2 Space Shift Keying (SSK) Modulation: On the Transmit- t a IEEE Int. Commun. Conf. N N · ”, ٣ H the if =N t ) Main Result: Transmit-Diversity > 2 Transmit-Diversity Main Result: Rlog Div2 ٣ H be the size of the ٣ (N 2 H log = antenna-elements and the subsets are pairwise disjoint Let N such that N Then, theR Rlog system achievesThis scheme transmit-diversity isset called partitioning (TOSD-GSSK-SP) TOSD-GSSK with mapping by pairwise disjoint Result 3 (Div>2) Transmit-Diversity for SM (25/61) Transmit-Diversity Diversity/Multiplexing Trade-Off M. Di Renzo and H. Haas, “ 256 (.) (.) (.) (.) 1 3 2 4 w w w w no mod. no mod. no mod. no mod. 1 0 , June 2011. =2, R=1, Div=4 a SP - Space Shift Keying (SSK) Modulation: On the Transmit- =4, N t IEEE Int. Commun. Conf. N GSSK ”, - AI-1 = 1 AI-2 = 0 TOSD 1 - AI 2 - Transmit-Diversity for SM (26/61) Transmit-Diversity Diversity/Multiplexing Trade-Off M. Di Renzo and H. Haas, “ AI 257 a N · 2 v= (.), the spatial- i Di , t N (.), Div=2 i f (.), Div=2 i on o , June 2011. i t ii i orthogonal w art t p orthogonal w sa t i orthogonal w (.), Div=1 i t >1, N a (.), Div=1 i N Space Shift Keying (SSK) Modulation: On the Transmit- >1, N a IEEE Int. Commun. Conf. (.)=w agram =1, N 0 ”, a N (.)=w di 0 N on >1, w i a =1, w at a N ll i N SSK: GSSK: TOSD-SSK: TOSD-GSSK: TOSD-GSSK-SP: conste Five schemes are studied: Transmit-Diversity for SM (27/61) Transmit-Diversity Diversity/Multiplexing Trade-Off M. Di Renzo and H. Haas, “ 258 [dB] 0 /N m E R=4] , Div = 1 and Div = = 2 1 and Div Div Na=3 , [Nt=6 GSSK [Nt=5, Na=2, R=3] Na=2, [Nt=5, GSSK GSSK [Nt=6 Na=3 R=3] Na=2, [Nt=5, R=4] TOSD-GSSK R=4] Na=3, [Nt=6, TOSD-GSSK 10 15 20 25 30 35 40 45 50 -1 -2 -3 -4 -5 10 10 10 10 10 EP B B A Transmit-Diversity for SM (28/61) Transmit-Diversity 259 4] v= Di , 2 a= N , 4 = [Nt 4 N Div=6] Na=3, [Nt=6, 2 Div=8] Na=4, Di[Nt=8, 4] [dB] 0 /N m E R = 1 - TOSD-GSSK-SP - = 1 R 5 10152025 -1 -2 -3 -4 -5 10 10 10 10 10 EP B B A Transmit-Diversity for SM (29/61) Transmit-Diversity 260 6] v= Di , 3 a= N , 6 = Div=1] , [Nt SP - Na=1 , GSSK - [Nt=2 [dB] 0 SSK [Nt=2 Na=1 Div=2] Na=1, [Nt=2, Div=1] TOSD-SSK Div=4] Na=2, TOSD-GSSK-SP[Nt=4, TOSD GSSK SP [Nt Div=8] Na=4, [Nt=8, 6TOSD-GSSK-SP N 3 Di 6] /N R = 1 m E 0 5 10 15 20 25 30 35 40 45 50 0 -1 -2 -3 -4 -5 10 10 10 10 10 10 EP B B A Transmit-Diversity for SM (30/61) Transmit-Diversity 261 6] v= Di , 3 a= N , 1] 12 v= = Di , [Nt 1 = SP - Na , 4 = GSSK - [Nt [dB] 0 /N R = 2 SSK [Nt 4 Div=2] Na=1, [Nt=4, NaTOSD-SSK 1 Div=4] Na=2, [Nt=8, DiTOSD-GSSK-SP 1] TOSD GSSK SP [Nt 12 N 3 Di 6] m E 0 5 10 15 20 25 30 35 40 45 50 0 -1 -2 -3 -4 -5 10 10 10 10 10 10 EP B B A Transmit-Diversity for SM (31/61) Transmit-Diversity 262 SSK [Na=1, R=3, Div=1] R=3, SSK [Na=1, TOSD-SSK [Na=1, R=3, Div=2] Div=1] R=6, [Na=4, GSSK Div=2] R=6, [Na=4, TOSD-GSSK Div=4] R=2, [Na=2, TOSD-GSSK-SP Div=8] R=1, [Na=4, TOSD-GSSK-SP [dB] 0 /N Nt = 8 m E 0 5 10 15 20 25 30 35 40 45 50 2 0 -1 - -3 -4 -5 10 10 10 10 10 10 EP B B A Transmit-Diversity for SM (32/61) Transmit-Diversity 263 ] Nt=7, R=5, Div=2 [ [] GSSK [Nt=6, R=4, Div=1] R=4, [Nt=6, GSSK Div=1] R=5, [Nt=7, GSSK Div=2] R=4, [Nt=6, TOSD-GSSK TOSD-GSSK Div=6] R=1, [Nt=6, TOSD-GSSK-SP Div=6] R=2, [Nt=12, TOSD-GSSK-SP [dB] 0 /N Na = 3 m E 0 5 10 15 20 25 30 35 40 45 50 0 -1 -2 -3 -4 -5 10 10 10 10 10 10 EP B B A Transmit-Diversity for SM (33/61) Transmit-Diversity 264 ode) ti c SM ou S1 S1 S2 S2 for fAlam * * , June 2011. o * * n S1 S1 -S2 -S2 diversity o - i s n e xt (e e s o transmit 2 o of STBC STBC lamouti al t Alamouti A IEEE Int. Commun. Conf. equ ”, y it s challenges r e 0 1 iv d desy design mit- s the Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- in tran ed t S1 es r e eesed S2 Understanding Generalizing the TOSD approach to SMInt (TOSD-SM) From SSK to SM Challenges (…let us start, e.g., from Alamouti…) AI Transmit-Diversity for SM (34/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes H 265 N ≤ h diversity - antennas - transmit transmit , June 2011. active analyzed the have be t a a N N N and 2 log IEEE Int. Commun. Conf. and 2 ”, 2 = a symbol) - H N N for antennas 2 - is modulated , Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- transmit diversity - the index . Find the actual spatial constellation diagram of size N - ogy l be t o d N Transmit Transmit-diversity can be achieved with single-stream decoding complexity o hdl h Let Then, the largest possible size of the spatial-constellation diagram is: Objective such that: We have computed the(antenna PEP (Pairwise Errorand Probability) single-stream decoding of optimality of any pair each of of them Problem statement Met Transmit-Diversity for SM (35/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes e a of , h 266 t r f transmit- the spatial- . However is of the orde , June 2011. So, no single-stream y lexit component on top o p py are all the same, sets of antennas g r SS SSK com in e r h pp g ing t IEEE Int. Commun. Conf. dd ”, esame,a h t ram into non-overla ll g Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- are a r Main Result: Same Shaping FiltersMain Result: at Tx can be used and the receive correlations r Na M · h Whatever the spatial-constellation diagram is,transmitte if the shaping filters at the N If the shaping filters at themulti-stream transmitte receiverdecoding is needed at the destination for ML-optimum Alamouti code destroys its inherent orthogonality. decode diversity equal to 2 can be guaranteed by partitioning constellation dia Result 1 (receiver complexity) Result 2 (transmit-diversity) Transmit-Diversity for SM (36/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes 267 S1 S1 S2 S2 * * , June 2011. * * S1 S1 -S2 -S2 STBC STBC Alamouti Alamouti IEEE Int. Commun. Conf. ”, 0 1 Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- Same Shaping Filters at Tx – Example S1 S2 FromResult1andResult2,itfollowsthatthisschemeachieves transmit-diversity equal to 2 but multi-stream decoding is needed AI Transmit-Diversity for SM (37/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes 268 the ea b at the ld both ou zero cross- hld h guaranteed s of be m a r can ag choice , June 2011. di r m n o ti a decoding ll e adequate t s ntlltin n an stream -co l - via a ti IEEE Int. Commun. Conf. a ptil p ”, s single ) e and the spatial-constellation diagram ty th i guaranteed nd vers ,a be n di i ) complexity - o on t- i ti i c can low Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- n 2 u In particular, some pairs of filters should have unct fntin f fi f . In particular, some pairs of filters must have zero cross- n of on o transm i ti (i ( at a li l l 4 optimum e - t rr l ti n l Main Result: Time-OrthogonalMain Result: Shaping Filters at Tx partition of the transmit-antenna array ML via antransmitter. adequate choicecorre of the (precoding) shaping filters atML-optimum the low-complexitydiversity single-stream decodingprecoding with shaping filters transmit- co transmitter esu Result 3 (receiver complexity) Rl R Transmit-Diversity for SM (38/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes 269 ) ) . . (.) (.) () ( () ( 1 1 2 2 w w w w g S1 S1 S2 S2 , June 2011. * * * * S2 S1 S1 - -S2 le-stream decodin g IEEE Int. Commun. Conf. STBC STBC ”, Alamouti Alamouti to 2 with sin l 0 1 ua q e y Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- S1 Time-OrthogonalFilters Shaping at Tx – Example S2 From Result 3transmit-diversit and Result 4, it follows that this scheme achieves Transmit-Diversity for SM (39/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes AI 270 sa d TOSD- nee ( different d g ) an 1 usin o y t STBC l - , June 2011. SM ( equa nts ity i o p vers f di it hisisobtainedb - it T at the transmitter along with a spatial- ng sets o i ransm tit t IEEE Int. Commun. Conf. ”, app li l eves hi over ac h t ih h wi c hi h whi which achieves transmit-diversity equal to 2 and needs a in all the antennas at the transmitter along with a spatial- agram Transmit-Diversity for Spatial Modulation (SM): Towards the Design of High- up, t di on ) i at ll i -case se t le-stream decoder at the destination. g ors lamouti code (rate=1) SM-STBC multi-stream decoder at the destination. It is obtained by using the same Wt W shaping filters conste Best-case setup, and time-orthogonal shapingconstellation filters diagram composed by non-overlapping sets of points SM A H3 and H4 OSTBCs (rate=3/4) sin Case studies Baseline schemes Transmit-Diversity for SM (40/61) Transmit-Diversity M. Di Renzo and H.Rate Haas, Spatially-Modulated “ Space-Time Block Codes 271 M=4] , Nh=4 , [Nt=4 [M=8] STBC - Alamouti [M=8] [Nt=2,SM M=4] [Nt=4,SM M=2] SM STBC [Nt=4 M=2] Nh=4 Nh=16, [Nt=7, SM-STBC M=4] TOSD-SM-STBC [Nt=8, Nh=4, M=4] [dB] 0 /N m 3 bits/s/Hz 3 E 0 5 10 15 20 25 30 35 40 45 50 0 -1 -2 -3 -4 -5 10 10 10 10 10 10 EP B B A Transmit-Diversity for SM (41/61) Transmit-Diversity 272 [dB] 0 16] /N = m M 5 bits/s/Hz 5 E , 4 = Nh , 4 = [Nt STBC - lamouti [M=32] lamouti A [Nt=2,SM M=16] [Nt=8,SM M=4] SM STBC [Nt M=8] Nh=16, 4 [Nt=7, SM-STBC Nh 4 M Nh=4, [Nt=8, M=16] TOSD-SM-STBC 16] 0 5 10 15 20 25 30 35 40 45 50 0 -1 -2 -3 -4 -5 10 10 10 10 10 10 EP B B A Transmit-Diversity for SM (42/61) Transmit-Diversity 273 M=2] , Nh=2 , [Nt=4 STBC [M=4] - SM H3 - - STBC H3 [M=4] STBC-H4 [M=4] SM-STBC [Nt=3, Nh=2, M=2] TOSD SM STBC [Nt=4 Nh=2 M=2] [dB] 0 /N m E 1.5 bits/s/Hz 1.5 0 5 10 15 20 25 30 35 40 45 50 2 0 -1 - -3 -4 -5 10 10 10 10 10 10 BEP A Transmit-Diversity for SM (43/61) Transmit-Diversity 274 [dB] 0 16] /N = m M E , 4.5 bits/s/Hz 4.5 2 = Nh , 3 = [Nt STBC - STBC-H3 [M=64] STBC-H3 [M=64] STBC-H4 SM STBC [Nt M=8] Nh=8, 3 [Nt=5, SM-STBC Nh 2 M Nh=2, [Nt=4, M=16] TOSD-SM-STBC 16] 0 5 10 15 20 25 30 35 40 45 50 2 0 -1 - -3 -4 -5 10 10 10 10 10 10 BEP A Transmit-Diversity for SM (44/61) Transmit-Diversity 275 IEEE ”, Space–time block coded spatial modulation , vol. 59, no. 3, pp. 823–832, Mar. 2011. Transmit-Diversity for SM (45/61) Transmit-Diversity Trans. Commun. E. Basar, U. Aygolu, E. Panayirci, and H. V. Poor, “ 276 IEEE ”, Space–time block coded spatial modulation , vol. 59, no. 3, pp. 823–832, Mar. 2011. Example: - Nt = 4 - BPSK Alamouti - = 2 bpcu R Transmit-Diversity for SM (46/61) Transmit-Diversity Trans. Commun. E. Basar, U. Aygolu, E. Panayirci, and H. V. Poor, “ 277 IEEE ”, Space–time block coded spatial modulation , vol. 59, no. 3, pp. 823–832, Mar. 2011. Transmit-Diversity for SM (47/61) Transmit-Diversity Trans. Commun. E. Basar, U. Aygolu, E. Panayirci, and H. V. Poor, “ 278 Transmit-Diversity for SM (48/61) Transmit-Diversity 279 Transmit-Diversity for SM (49/61) Transmit-Diversity 280 Transmit-Diversity for SM (50/61) Transmit-Diversity 281 Transmit-Diversity for SM (51/61) Transmit-Diversity 282 time code with – rate space – l 2×2ful A , vol. 51, no. 4, pp. 1432–1436, Apr. 2005. he golden code: T iterbo/perfect_codes/Golden_Code.html The Golden Code iterbo, “ V IEEE Trans. Inform. Theory ”, C. Belfiore, G. Rekaya, and E. Transmit-Diversity for SM (52/61) Transmit-Diversity – J. nonvanishing determinants http://www.ecse.monash.edu.au/staff/ev 283 ,pp. eme: h sc d e dd d co k oc bl k me i t – rate space h IEEE Wireless Commun. Netw. Conf. g ”, Hi h “ , idl m h c ShS idl . M . T d ,an k a b a DbkD . G . A , i Double Space-Time Transmit Diversity (DSTTD) Diversity Double Space-Time Transmit nggosanus Oi O . N Transmit-Diversity for SM (53/61) Transmit-Diversity . E Performance and improvement in194–199, correlated Mar. 2002. fading channels 284 Transmit-Diversity for SM (54/61) Transmit-Diversity 285 Transmit-Diversity for SM (55/61) Transmit-Diversity 286 QAM x 2 IQ IQ arctan 2 j p ex 22,1, 11,2, ss js ss js sj 2 0 s , Nov. 2012. 1 Modulation diversity for spatial modulation using complex interleaved 0 s channel uses channel IEEE TENCON ”, SM-CIOD: Transmit-Diversity with a Single-RF Chain with a Single-RF Transmit-Diversity SM-CIOD: Transmit-Diversity for SM (56/61) Transmit-Diversity antennas R. Rajashekar and K. V. S. Hari, “ orthogonal design 287 used is used t N mod (l+1) mod N is used l antenna use: channel Second channel use: antenna - Firstantenna channel use: - , Nov. 2012. Modulation diversity for spatial modulation using complex interleaved IEEE TENCON ”, SM-CIOD: Transmit-Diversity with a Single-RF Chain with a Single-RF Transmit-Diversity SM-CIOD: Transmit-Diversity for SM (57/61) Transmit-Diversity R. Rajashekar and K. V. S. Hari, “ orthogonal design 288 antennas 1 CBS + 1 antennas 2 t t N - -N , Nov. 2012. Modulation diversity for spatial modulation using complex interleaved IEEE TENCON ”, SM-CIOD: Transmit-Diversity with a Single-RF Chain with a Single-RF Transmit-Diversity SM-CIOD: Transmit-Diversity for SM (58/61) Transmit-Diversity R. Rajashekar and K. V. S. Hari, “ orthogonal design 289 Phase Rotations , Nov. 2012. Modulation diversity for spatial modulation using complex interleaved IEEE TENCON ”, Transmit-Diversity for SM (59/61) Transmit-Diversity R. Rajashekar and K. V. S. Hari, “ orthogonal design 290 Transmit-Diversity for SM (60/61) Transmit-Diversity 291 Transmit-Diversity for SM (61/61) Transmit-Diversity 292 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 293 ehicular V ransactions on T IEEE ots l ”, me-s i active transmit-antennas t α s ransmitter N T 75/74/PDF/Correction_TransmitDiversitySM.pdf On Transmit–Diversity for Spatial Modulation MIMO: Impact of Spatial– ve-antennas i , Vol. 62, No. 6, pp. 2507–2531, July 2013. – See “Correction Paper” too: rece transmit-antennas N t r N N Spatially-Modulated Space-Time-Coded MIMO (1/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Technology http://hal.archives-ouvertes.fr/docs/00/84/ – 294 IEEE Transactions on Vehicular ”, for Spatial Modulation MIMO: Impact of Spatial y Diversit – ransmit T On , Vol. 62, No. 6, pp. 2507–2531, July 2013 Spatially-Modulated Space-Time-Coded MIMO (2/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology – 295 IEEE Transactions on Vehicular ”, for Spatial Modulation MIMO: Impact of Spatial y Diversit – ransmit T On , Vol. 62, No. 6, pp. 2507–2531, July 2013 Spatially-Modulated Space-Time-Coded MIMO (3/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology – 296 IEEE Transactions on Vehicular ”, for Spatial Modulation MIMO: Impact of Spatial y Diversit – ransmit T On , Vol. 62, No. 6, pp. 2507–2531, July 2013 Spatially-Modulated Space-Time-Coded MIMO (4/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology – 297 IEEE Transactions on Vehicular ”, for Spatial Modulation MIMO: Impact of Spatial y Diversit – ransmit T On , Vol. 62, No. 6, pp. 2507–2531, July 2013 Spatially-Modulated Space-Time-Coded MIMO (5/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology – 298 IEEE Transactions on Vehicular ”, for Spatial Modulation MIMO: Impact of Spatial y Diversit – ransmit T On , Vol. 62, No. 6, pp. 2507–2531, July 2013 Spatially-Modulated Space-Time-Coded MIMO (6/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology 299 Spatially-Modulated Space-Time-Coded MIMO (7/23) 300 Spatially-Modulated Space-Time-Coded MIMO (8/23) 301 SWOSF – SetPart – SMSTT – M2 T and OSF – SetPart – ML-Optimum Single-Stream Decoding: SMSTT – M2 T Spatially-Modulated Space-Time-Coded MIMO (9/23) – 302 SWOSF – SetPart – IEEE Transactions on Vehicular ”, SMSTT – M2 T for Spatial Modulation MIMO: Impact of Spatial y and Alamouti Diversit – OSF – ransmit T On SetPart – ML-Optimum Single-Stream Decoding: SMSTT – , Vol. 62, No. 6, pp. 2507–2531, July 2013 M2 T Spatially-Modulated Space-Time-Coded MIMO (10/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology – 303 IEEE Transactions on Vehicular ”, for Spatial Modulation MIMO: Impact of Spatial y Diversit – ransmit T On , Vol. 62, No. 6, pp. 2507–2531, July 2013 Spatially-Modulated Space-Time-Coded MIMO (11/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology – 304 SWOSF – SetPart – IEEE Transactions on Vehicular H3 ”, - SMSTT kh – aro T M2 T for Spatial Modulation MIMO: Impact of Spatial y and OSTBC Diversit e: – l OSF – ransmit xamp T ElOSTBCTkh E On SetPart – ML-Optimum Single-Stream Decoding: SMSTT – , Vol. 62, No. 6, pp. 2507–2531, July 2013 M2 T Spatially-Modulated Space-Time-Coded MIMO (12/23) M. Di Renzo andConstellation H. Diagram Haas, “ and Shaping Filters at the Transmitter Technology 305 = 1 –= 1 = 4 bpcu) R r Diversity Analysis (N Diversity Spatially-Modulated Space-Time-Coded MIMO (13/23) 306 = 2 – = 4 bpcu) R r Diversity Analysis (N Diversity Spatially-Modulated Space-Time-Coded MIMO (14/23) 307 Multi vs. Single-Stream Multi vs. Single-Stream (R = 4 bpcu) Decoding Spatially-Modulated Space-Time-Coded MIMO (15/23) 308 = 1 r N R = 4 bpcu Spatially-Modulated Space-Time-Coded MIMO (16/23) 309 = 1 r N R = 6 bpcu Spatially-Modulated Space-Time-Coded MIMO (17/23) 310 = 2 r N R = 6 bpcu Spatially-Modulated Space-Time-Coded MIMO (18/23) 311 = 4 r N R = 6 bpcu Spatially-Modulated Space-Time-Coded MIMO (19/23) 312 = 1 r N R = 8 bpcu Spatially-Modulated Space-Time-Coded MIMO (20/23) 313 = 2 r N R = 8 bpcu Spatially-Modulated Space-Time-Coded MIMO (21/23) 314 = 4 r N R = 8 bpcu Spatially-Modulated Space-Time-Coded MIMO (22/23) 315 = 2 r R = 8 bpcu OSF-MIMO N Spatially-Modulated Space-Time-Coded MIMO (23/23) 316 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 20 317 18 16 single-hop 14 multi-hop 12 10 8 Signal-to-Noise-Ratio [dB] 6 4 2 0 0 -1 -2 -3 10 10 10 10 ility b b Proba Error Time-Slot 1 Time-Slot 2 : additional : better performance, -duplex constraint… f resources (relays, time-slots, frequencies), capacity reduction, hal extended coverage… Advantages Disadvantages Relay-Aided SM (1/24) Relay-Aided Multi-Hop Networks: 318 20 18 16 cooperative 14 non-cooperative 12 10 8 6 Signal-to-Noise-Ratio [dB] 4 2 … 0 3 0 -1 -2 - -4 -5 10 10 10 10 10 10 ty t Probabili Error constraint x ple u dple d - half , : additional resources (relays, time-slots, frequencies), ction u : better performance, (macro) diversity… Time-Slot 1 red ction Advantages Disadvantages capacit capacity Relay-Aided SM (2/24) Relay-Aided Cooperative Networks: ”, 319 Dual–hop spatial modulation (Dh–SM) , pp. 1–5, May 2011. Demodulate-and-Forward (DemF) Demodulate-and-Forward Dual-Hop Spatial Modulation Relay-Aided SM (3/24) Relay-Aided N.Serafimovski.,S.Sinanovic.,M.DiRenzo,andH.Haas,“ IEEE Veh. Technol. Conf. – Spring 320 Relay-Aided SM (4/24) Relay-Aided 321 Relay-Aided SM (5/24) Relay-Aided 322 Relay-Aided SM (6/24) Relay-Aided 323 Relay-Aided SM (7/24) Relay-Aided 324 Relay-Aided SM (8/24) Relay-Aided no for 325 or a distributed activated f multiple is rocess conveys p so PSK) the role o relay and or y relays. Each symbol has , la R p py the s y (QAM occur -activation y then bits ) , may R ID (N 2 hus, the rela he rela In TS-1, MS broadcaststo its a own group infolog symbol of N The relayswithout any coordination decode among them theEach relay received isthe assigned symbol symbol received an from individualthe MS ID. coincides with If transmission T spatial constellation diagram T Errors relays may wake up information BS Virtual SM-MIMO for the Uplink R1 R2 R3 R4 Distributed Relay-Aided SM (9/24) Relay-Aided MS spatial-constellation diagram 326 Demodulator Conventional SSK Demodulator Distributed Space Shift Keying for the Uplink of , Sep. 2012. IEEE CAMAD ”, Virtual SM-MIMO for the Uplink Relay-Aided SM (10/24) Relay-Aided S. Narayanan, M. Di Renzo, F. Graziosi, and H. Haas, “ Relay-Aided Cellular Networks 327 Distributed Space Shift Keying for the Uplink of , Sep. 2012. IEEE CAMAD ”, Optimal (Error-Aware) Demodulator Relay-Aided SM (11/24) Relay-Aided S. Narayanan, M. Di Renzo, F. Graziosi, and H. Haas, “ Relay-Aided Cellular Networks 328 Distributed Space Shift Keying for the Uplink of , Sep. 2012. IEEE CAMAD ”, Optimal (Error-Aware) Demodulator Relay-Aided SM (12/24) Relay-Aided S. Narayanan, M. Di Renzo, F. Graziosi, and H. Haas, “ Relay-Aided Cellular Networks 329 Relay-Aided SM (13/24) Relay-Aided 330 BS R1 R2 BS R2(R2) A new relaying protocol MS based on Spatial Modulation based on Spatial (the Relays have data in their buffers)have (the Relays BS BS R1(R1) R2(R2) … BS BS BS BS BS BS Relaying silent silent R2) , is is MS2*) id=MS2 Based ‐ Phoenix R1(R1) R2(MS) Rnid ‐ Rid(Rid) R2(MS1*) R2(MS R2) R1( Relaying Based Based Repetition Spectral-Efficient Relaying BS BS BS BS BS BS Based silent silent R1) –Alamouti , is is Selective (NC) id=MS1 Modulation Modulation R1(MS) Rnid Rid(Rid) R1(MS1) R2(MS2) R1(MS R1) Rbest(MS) x Coding Relaying Rx R Rx Rx Rx Rx Spatial DSTBC Network MS(MS) MS(MS) MS(MS) MS(MSi) MS(MS2) MS(MS1) Relay-Aided SM (14/24) Relay-Aided 331 Distributed Spatial Modulation for Relay Distributed SM , Sep. 2013. IEEE VTC-Fall ”, Relay-Aided SM (15/24) Relay-Aided S. Narayanan, M. Di Renzo, F. Graziosi, and H. Haas, “ Networks 332 Distributed Spatial Modulation for Relay × , Sep. 2013. Optimal (Error-Aware) Demodulator IEEE VTC-Fall ”, Relay-Aided SM (16/24) Relay-Aided S. Narayanan, M. Di Renzo, F. Graziosi, and H. Haas, “ Networks 333 Diversity order ofDiversity 2 the source is (analytically proved) Relay-Aided SM (17/24) Relay-Aided 334 SPM Relay-Aided SM (18/24) Relay-Aided 335 Relay-Aided SM (19/24) Relay-Aided 336 ,vol.10,no.7,pp. Relaying Listening Phase IEEE Trans. Wireless Commun. Non-Orthogonal ", ransmission in Decode-and-Forward Relaying Systems: T Relayed Information Information-Guided issa, " A Decode-and-Forward (DF) Decode-and-Forward ang and S. Y Relay-Aided SM (20/24) Relay-Aided . Spatial Exploitation and Throughput Enhancement Y 2341-2351, July 2011. 337 ,vol.10,no.7,pp. Relaying : received from the source : received ] c x , d x [ : spatial-constellation diagram : signal-constellation diagram d c x = x x Relaying Phase - - - IEEE Trans. Wireless Commun. Non-Orthogonal ", ransmission in Decode-and-Forward Relaying Systems: T Information-Guided Non-Relayed InformationNon-Relayed issa, " A Decode-and-Forward (DF) Decode-and-Forward ang and S. Y Relay-Aided SM (21/24) Relay-Aided . Spatial Exploitation and Throughput Enhancement Y 2341-2351, July 2011. ” 338 le- g s, l anne h ) ve c i : g in [*] SR-IGT ) ex cooperat l ecific IGT case with sin ecific IGT case p pg benchmark up arison amon dld i h l” p pg - he s lf T The general IGT scheme The general The The a (b) M = 4 relaynodes (a) M = 2 relay nodes general IGT Capacity complementary cumulative distribution function (CCDF) com - ( - relay selection ( and P. H. El Gamal, [*] K. Azarian, “On the achievable Schniter, tradeoffdiversity-multiplexing in hlf h IEEE Trans. Inf. Theory, vol. 51, no. vol. Inf. Theory, IEEE Trans. 4152–4172, Dec.12, pp. 2005. - Relay-Aided SM (22/24) Relay-Aided 339 Relay-Aided SM (23/24) Relay-Aided 340 Relay-Aided SM (24/24) Relay-Aided 341 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 342 SM in Heterogeneous Cellular Networks (1/22) Cellular Networks SM in Heterogeneous or f 343 d te i o lil d or exp d/ an d ico, femto, relays, DAEs, p manage l ly roper p e b ld ou hld h erence s f Overlaid multi-tier heterogeneous scenario Overlaid multi-tier heterogeneous nter if i us, cognitive radios, etc.) reliable communications and energy efficiency Heterogeneous cellular systems arecells networks providing with different different QoSand types requirements contend of to the the wireless users, medium which (macro, coexist Th SM in Heterogeneous Cellular Networks (2/22) SM in Heterogeneous Cellular Networks 344 …what cellular will migrate…what to (Prof. Jeff Andrews, UT Austin)… SM in Heterogeneous Cellular Networks (3/22) SM in Heterogeneous Cellular Networks , ar l u 345 ll e Clll C IEEE Trans. ”, eterogeneous )are: IEEE Trans. Commun. H ”, k n li Error Performance of Multi– integrations own Dlik D f ate o and/or R hts g abstraction models : verage insi A r “ o / A Tractable Approach to Coverage and Rate in Cellular ferers – A Stochastic Geometry Approach simulations models orazza, C . E . , vol. 59, no. 11, pp. 3122–3134, Nov. 2011. G numerical d ,an i abstraction ott id u intensive GidiG these . , A IEEE Trans. Commun. , Vol. 61, No. 5, pp. 2025–2047, May 2013. ”, enzo, The Wyner model The single-cell interfering model The regular hexagonal or square grid model Are over-simplistic and/or inaccurate Require Provide information only for specific BSsNo deployments closed-form solutions and R Di Conventional(heterogeneous) approaches cellular networks ( for the analysis andHowever design of SM in Heterogeneous Cellular Networks (4/22) SM in Heterogeneous Cellular Networks . Networks over Generalized Fading ChannelsVol. – 61, A No. Stochastic 7, pp. Geometry 3050-3071, Approach July 2013. Networks J. G. Andrews, F. Baccelli, andM.DiRenzo,C.Merola,A.Guidotti,F.Santucci,andG.E.Corazza,“ R. K. Ganti, “ M Antenna Receivers inCommun. a Poisson Field of Inter 346 timization p for Heterogeneous Cellular and o , n g of HCNs MODEL desi , Stochastic Geometry sis y y, g, p An Emerging (Tractable) Approach An Emerging (Tractable) emerges as an effective tool for emerges as an effective SPATIAL MODEL anal K-tier network with BSPoisson Point locations Processes modeled (PPPs) as independentPPP marked model islower bound surprisingly to reality good and trendsPPPmakesevenmoresenseforHCNsduetolessregularBSs for still hold 1-tier asplacements for well lower tiers (macro (femto, etc.) BSs): RANDOM SPATIAL Networks (HCNs): SM in Heterogeneous Cellular Networks (5/22) SM in Heterogeneous Cellular Networks 347 l How It Works (Downlink – (Downlink It Works How 1-tier) na i e term bil e mo b ro PbP bil i l PPP-distributed macrostation base SM in Heterogeneous Cellular Networks (6/22) SM in Heterogeneous Cellular Networks 348 Useful link l How It Works (Downlink – (Downlink It Works How 1-tier) na i e term bil e mo b ro PbP bil i l PPP-distributed macrostation base SM in Heterogeneous Cellular Networks (7/22) SM in Heterogeneous Cellular Networks 349 k n li l u f se Ufllik U l How It Works (Downlink – (Downlink It Works How 1-tier) na i e term bil e mo b ro PbP bil i l PPP-distributed macrostation base SM in Heterogeneous Cellular Networks (8/22) SM in Heterogeneous Cellular Networks 350 Useful link l How It Works (Downlink – (Downlink It Works How 1-tier) na i e term bil e mo b ro PbP bil i l PPP-distributed macrostation base SM in Heterogeneous Cellular Networks (9/22) SM in Heterogeneous Cellular Networks , ar l 351 u ll e Clll C erogeneous t e IEEE Trans. Commun. Ht H ”, k n li own Dlik D f eo t a Rt R , vol. 11, no. 10, pp. 3484–3495, Oct. 2012. verage A “ orazza, C . E . G d How It Works (Downlink – (Downlink It Works How 2-tier) ,an IEEE Trans. Wireless Commun. tti ”, o id Heterogeneous Cellular Networks with Flexible Cell Association: A Comprehensive u G id tti . A enzo, R Di SM in Heterogeneous Cellular Networks (10/22) Cellular Networks SM in Heterogeneous . Networks over Generalized Fading ChannelsVol. – 61, A No. Stochastic 7, pp. Geometry 3050-3071, Approach July 2013. J. G. Andrews etDownlink al., SINR “ Analysis M . p 352 le Ma g gp ’s are Δ stations Base station distribution City, in Taipei Taiwan, shown on Goo Blue the locations of base stations based models for modeling cellular y , 10 pages, Oct. 2012. Stochastic geometr S. Chen, “ – . Y Springer Wireless Netw. ”, . Shihet, and Y – Worldwide Base Station Locations Available via OpenCellID Station Locations Available Base Worldwide H. Lee, C. SM in Heterogeneous Cellular Networks (11/22) Cellular Networks SM in Heterogeneous – Open source project OpenCellID: http://www.opencellid.org/ C. networks in urban areas 353 based models for modeling cellular y , 10 pages, Oct. 2012. Stochastic geometr S. Chen, “ – . Y East Asia Springer Wireless Netw. ”, PPP better (or same accuracy Hexagonalthan as) . Shihet, and Y – H. Lee, C. SM in Heterogeneous Cellular Networks (12/22) Cellular Networks SM in Heterogeneous – Open source project OpenCellID: http://www.opencellid.org/ C. networks in urban areas 354 based models for modeling cellular y , 10 pages, Oct. 2012. Stochastic geometr S. Chen, “ – . Y South Asia Springer Wireless Netw. ”, PPP better (or same accuracy Hexagonalthan as) . Shihet, and Y – H. Lee, C. SM in Heterogeneous Cellular Networks (13/22) Cellular Networks SM in Heterogeneous – Open source project OpenCellID: http://www.opencellid.org/ C. networks in urban areas 355 based models for modeling cellular y , 10 pages, Oct. 2012. Stochastic geometr S. Chen, “ – . Y Europe Springer Wireless Netw. ”, PPP better (or same accuracy Hexagonalthan as) . Shihet, and Y – H. Lee, C. SM in Heterogeneous Cellular Networks (14/22) Cellular Networks SM in Heterogeneous – Open source project OpenCellID: http://www.opencellid.org/ C. networks in urban areas 356 based models for modeling cellular y , 10 pages, Oct. 2012. Stochastic geometr S. Chen, “ – . Y America Springer Wireless Netw. ”, PPP better (or same accuracy Hexagonalthan as) . Shihet, and Y – H. Lee, C. SM in Heterogeneous Cellular Networks (15/22) Cellular Networks SM in Heterogeneous – Open source project OpenCellID: http://www.opencellid.org/ C. networks in urban areas 357 ons i Useful link (SM) ase stat b ) cell association is neglected emto f e.g., e.g., ( er i(i f )b i ower-t l ng i er Preliminary Reference Scenario f nter i d ute ib Interfering link (QAM/PSK/SSK/SM) str di ib d i f i l Tagged macro base station at a fixed macrodistance base station at a fixed Tagged Probe mobile terminal PPP- SM in Heterogeneous Cellular Networks (16/22) Cellular Networks SM in Heterogeneous 358 I b 1 ggregate Interference ** 12 II I II BG SS b I b AWGN A I b cos 2 , 1 , ,, Eexp exp 0, 4 U 2Re N 2Re I 1 2 Sb b 000AGG Useful Signal II I i BB I b i II II I Z d B Ms sB s GCN c PPP i Λ etr i A KeyA from Result Stochastic GeometryTheoryPPP and Decision Mi M AGG AGG SM in Heterogeneous Cellular Networks (17/22) Cellular Networks SM in Heterogeneous II 2, 359 II BG I B Aggregate Interference I Equivalent AWGN Equivalent conditioning upon conditioning d) **12 erre f AWGN pre ( y ll Equivalent AWGN Channel AWGN Equivalent U 2Re N 2Re ca 2 ti y 000 ltill(l f d) Useful Signal applied by conditioning upon B or ana on : The conditioning can be removed either numerically : Thecan be removed conditioning : The frameworks developed without interference can be : The developed frameworks i s i Λ ec Metric Dii D STEP 1 STEP 2 SM in Heterogeneous Cellular Networks (18/22) Cellular Networks SM in Heterogeneous , ce 61 or n f 360 . a ., rm Vol g o , . rf e. e ( Prfrmn P r o ng i Technol Err r . “ us a, d Veh zz . a r o Crzz C . Trans E . G , i ecompute IEEE , b ucc ” nt a SntS i can . F can be obtained from: 2 , Channels , Vol. 61, No. 5, pp. 2025–2047, May 2013. tti o Bit Error Probability of Spatial Modulation (SM-) III id u Gidtti G STEP The Bottom Line . Fading STEP 1 A n i a, l I signal spatial joint o r B e MrlM . C Generalized IEEE Trans. Commun. o, ”, nz e over Rnz R I BBBB Di . of Multi–Antenna Receivers inApproach a Poisson Field of Interferers – A Stochastic Geometry No. 3, pp. 1124-1144, Mar. 2012. M. Di RenzoMIMO and H. Haas, “ M eaverageover Closed-form results in Th Nakagami-m fading): SM in Heterogeneous Cellular Networks (19/22) Cellular Networks SM in Heterogeneous ABEP ABEP ABEP ABEP f o 361 ld e Fi ld , Honolulu, sson i o Pi P na i MIMO on i at l u d o MdlM i l a i pat Sil S f so i ys l na Ali A ormance f er Pf P “ enzo, R Di . IEEE International Conference on Computing, Networking and Communications M ”, d uan L SM in Heterogeneous Cellular Networks (20/22) SM in Heterogeneous Cellular Networks . W Interferers USA, February 3–6, 2013 (submitted). f o 362 ld e Fi ld , Honolulu, sson i o Pi P na i MIMO on i at l u d o MdlM i l a i pat Sil S f so i ys l na Ali A ormance f er Pf P “ enzo, R Di . IEEE International Conference on Computing, Networking and Communications M ”, d uan L SM in Heterogeneous Cellular Networks (21/22) Cellular Networks SM in Heterogeneous . W Interferers USA, February 3–6, 2013 (submitted). f o 363 ld e Fi ld , Honolulu, sson i o Pi P na i MIMO on i at l u d o MdlM i l a i pat Sil S f so i ys l na Ali A ormance f er Pf P “ enzo, R Di . IEEE International Conference on Computing, Networking and Communications M ”, d uan L SM in Heterogeneous Cellular Networks (22/22) SM in Heterogeneous Cellular Networks . W Interferers USA, February 3–6, 2013 (submitted). 364 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 365 SM for Light Communications (1/13) Visible 366 SM for Light Communications (2/13) Visible 367 SM for Light Communications (3/13) Visible 368 , pp. 1853-1888, May 2012. Wireless Myths, Realities, and Proc. of the IEEE ", SM for Light Communications (4/13) Visible L. Hanzo, H. Haas, S. Imre, D. C. O'Brien, M. Rupp, L. Gyongyosi, " Futures: From 3G/4G to Optical and Quantum Wireless 369 , Dec. 2011. On the Performance of Space Shift Keying for Optical Wireless IEEE Globecom - Workshop on Optical Wireless Communications ,” SM for Light Communications (5/13) Visible T.Fath,M.DiRenzo,andH.Haas,“ Communications 370 , Dec. 2011. : receiver detector area 2 = 15°: Tx semi-angle = 15°: Rx semi-angle 2 / 1/2 1 Ф A=1cm Ψ On the Performance of Space Shift Keying for Optical Wireless Optical Wireless Setup and Channel IEEE Globecom - Workshop on Optical Wireless Communications ,” SM for Light Communications (6/13) Visible T.Fath,M.DiRenzo,andH.Haas,“ Communications 371 = 8, Rate = 5 bpcu t N SM for Light Communications (7/13) Visible 372 SM for Light Communications (8/13) Visible 373 SM for Light Communications (9/13) Visible 374 , vol. 61, no. 2, pp. 733–742, Feb. 2013. = 4, Rate = 4 bpcu r IEEE Trans. Commun. ,” = N t N Performance Comparison of MIMO Techniques for Optical Wireless SM for Light Communications (10/13) Visible T. FathCommunications in and Indoor Environments H. Haas, “ 375 , vol. 61, no. 2, pp. 733–742, Feb. 2013. = 4, Rate = 8 bpcu r IEEE Trans. Commun. ,” = N t N Performance Comparison of MIMO Techniques for Optical Wireless SM for Light Communications (11/13) Visible T. FathCommunications in and Indoor Environments H. Haas, “ 376 = 0.7 TX , vol. 61, no. 2, pp. 733–742, Feb. 2013. IEEE Trans. Commun. ,” = 4, Rate = 4, 8 bpcu, d r Performance Comparison of MIMO Techniques for Optical Wireless = N t N SM for Light Communications (12/13) Visible T. FathCommunications in and Indoor Environments H. Haas, “ 377 SM for Light Communications (13/13) Visible GSSK –VLC transmitter developed byhttp://purevlc.com/ the startup PureVLC 378 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. 379 y frequenc r frequency carrier GHz 2 @ (Bristol/UK) environment: 2x2 MIMO @ 2.3GHz carrie y scenario Urban Laborator MIMO channel sounder Post-processing Performance assessment via channel measurements Testbed implementation (NI-PXIe-1075 @ Heriot-Watt Univ. / UK) Experimental Evaluation ofExperimental Evaluation SM (1/31) , 380 Performance IEEE VTC-Fall ", .M.Grant," P . Beach, H. Haas, and A ang, M. W r 4 a i a × 4 p g a a f Channel Measurements ) Racal ◦ of sts o y i (UK), usin consists tter cons y i hompson, M. Di Renzo, C.-X. T . ositioned atop a building, W setup p etransm ounis, MEDAV RUSK channel sounder MIMO channelare taken measurements aroundBristol the center cit of The MIMO, with 20 MHzcentered at bandwidth 2 GHz Th 2m, districts of Bristol cit providing elevatedcentral coverage business and of commercial of dual polarized (±45 Xp651772 antennas separated by Y Experimental Evaluation ofExperimental Evaluation SM (2/31) . of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A , 381 Performance IEEE VTC-Fall ", .M.Grant," P . Beach, H. Haas, and A r y ve an i ang, M. W (PIFA) ece rir r g nt e r e Channel Measurements ,whichisbased Antennas diff r nt F wo t thus avoidin , r , which is equipped with 4 ve Inverted i , hompson, M. Di Renzo, C.-X. ece T rir r . W e on 4-dipoleshelmet mounted on a cycle A reference headset shadowing by the user Alaptop Printed fitted inside the backpanel of the display th ounis, At devices arefour used, antennas: both equipped with Y Experimental Evaluation ofExperimental Evaluation SM (3/31) . of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A , m 382 6 second Performance a IEEE VTC-Fall roughly ", line walking .M.Grant," P it ity ec user th straight faster than the coherence d the a y in with , days . Beach, H. Haas, and A nificantl head osen aroun g gy taken h his ang, M. W then different on is eed is si on p ons are c Channel Measurements ti but device , oca lti l t measurement locations reference hompson, M. Di Renzo, C.-X. T . the W same measuremen second sthemeasurements 58 At each location theand user walked, holdinglong, the until 4096 laptop channel in snapshots front wereA recorded of him path perpendicular to the first A time of thefour to channel, reduce measurement the noise measurementsOne are set of averaged measurementand in results a with groups second the set of laptopthe of and reference only device, the reference device measurements taken at ounis, Y . Experimental Evaluation ofExperimental Evaluation SM (4/31) of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A , h 383 s, eac t Performance se IEEE VTC-Fall t l ", .M.Grant," P measuremen t eren . Beach, H. Haas, and A diff t ang, M. 348 W f the 20 MHz bandwidth o g l a Channel Measurements t o ttl t annin p pg ” MIMO, where, by manipulating the original ” MIMO, which are the original 4x4 channel measurements es a id bins s y rov hompson, M. Di Renzo, C.-X. p T . Small-scale Large-scale uenc measurements, larger virtual MIMO systems are created “ “ W s q qy Thi containing 1024fre snapshots ofAs the a simulations are 4×4frequency carried out MIMO using bin flatmeasurement fading channel, snapshot channels, centered to a create with single the around narrowband 128 channe 2 GHz,Two MIMO test is cases are investigated: chosen from each ounis, Y . Experimental Evaluation ofExperimental Evaluation SM (5/31) of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A , , d % 384 1 ence of id i Performance snapshots), IEEE VTC-Fall level ", 1024 aps exper is the correlation t .M.Grant," P l γ anne significance hl h a containing ose c with h , (each . Beach, H. Haas, and A test sets ons w fit ti ang, M. W of oca lti l , Small-Scale MIMO : measurement goodness MIMO e l 348 -sca the ll squared - hompson, M. Di Renzo, C.-X. of coefficient) T . chi W out or sma is used to identify Rayleigh fading channels F Rayleigh fading are used The 20 fulfilled this requirement and are keptFor for further each processing locationestimated, the then transmit the andindices, decay receive is of correlation fitted todecay the matrices an correlation, are exponential based decay on model the ( antenna ounis, Y . Experimental Evaluation ofExperimental Evaluation SM (6/31) of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A , 385 are them Performance correlation of IEEE VTC-Fall ", Both . .M.Grant," P the second channel, average r retained lowest are the from the reference device r . Beach, H. Haas, and A matrices with ang, M. sets W and the othe Small-Scale MIMO correlation p : to p pp : s l actual the first channel and 0.36 and 0.75 fo measurement r the anne hl h c and two d e t hompson, M. Di Renzo, C.-X. a T l . Two measurement sets withmodel the lowest meanfrom square the laptop error device between the The measured decay coefficients forand the 0.99 transmitter fo and receiver are 0.41 The coefficient are kept One is from the la respectively W orre Cltd C Uncorrelated channels ounis, Y . Experimental Evaluation ofExperimental Evaluation SM (7/31) of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A , e l of 386 mobile Performance arge-sca l IEEE VTC-Fall the e ", transmitter th a e t that .M.Grant," P form ocrea such t to d , kept is . Beach, H. Haas, and reversed A eps are use t are ang, M. snapshot ng s W i assing the chi-squared goodness of fit test p each Large-Scale MIMO rocess channels p - from t os p ng i the locations original channel y ow hompson, M. Di Renzo, C.-X. ll T o . The terminal becomes the transmitting device One the virtualelements array. ThisTo results reduce the inelements correlation are a between kept using adjacent virtual a down-sampling channels,Onl factor array only of 256 4 with 1024 for the Rayleigh fading distribution are kept fllf i W e 1) 2) 3) 4) channel measurements from the original channel measurements: Th ounis, Y . Experimental Evaluation ofExperimental Evaluation SM (8/31) of Spatial Modulation over Correlated and Uncorrelated Urban Channel Measurements 2013. A 387 Experimental Evaluation ofExperimental Evaluation SM (9/31) 388 Experimental Evaluation ofExperimental Evaluation SM (10/31) 389 Experimental Evaluation ofExperimental Evaluation SM (11/31) 390 Experimental Evaluation ofExperimental Evaluation SM (12/31) 391 Experimental Evaluation ofExperimental Evaluation SM (13/31) l , t h na 392 ea i l eac g p iii l Bh B i . nc A iil i eor . r , to appear. h p M SM rant, e G . th recovers t M o t ) . x P R) R ng – di ang, W . (DSP X accor .- m d C IEEE Trans. Veh. Technol. h e t t i ", va ti t d enzo, gor lih l R Di ng a en ac . i th M s i ers, ’) rocess b 2 p x l am Tx) ‘T - Indoor Testbed Ch b d gna . il i P s an , l ’ h (DSP 1 e ta l x i g es (‘T di i l Mlh M . e R id s, enna i t transmitter ve s i an oun Yi Y the it . A at Practical Implementation of Spatial Modulation , erece h ki ransm tit t y, t l h movs fi ac ast The binary dataalgorithm to be broadcastThe processed is data is first then(NI)-PXIe passed chassis passed to (PXIe-Tx) the through physical transmitter theEh E on digital the National signal Instruments a processing carrier frequency of 2.3The GHz receiver thenLl L detects anddata processes stream the radio frequency (RF) signal in PXIe–Rx. era Sfiki S . Experimental Evaluation ofExperimental Evaluation SM (14/31) and H. Haas, " N IEEE Early Access. 393 Experimental Evaluation ofExperimental Evaluation SM (15/31) 394 Antenna Spacing (Line-of-Sight Scenario) Experimental Evaluation ofExperimental Evaluation SM (16/31) 395 hile w requency Power’ to f stationary l a a na h g t for ih Si wi g ms A large roll-off factor 7 ong l ~ ,a d owerisfocusedinashort e p dd d typically en a is h ermits up-sampling of the data p st i which on hich i w he up-sampling ratio is set to four and the up- ith a factor labelled ‘Tunin to ensure that the mat T ii i w y est channel l lied ower. p erformed p the p l anne of hl h are necessar or c f time y adding is d p ector is multi use v l g gna coherence il i Digital Signal Processing for Transmission (DSP–Tx) Digital Signal Processing for Transmission the ot s il pil he resultin sampleddataisthenpassedthrougharootraisedcosine(RRC)finiteimpulse response (FIR) filter with 40 taps and a roll-off factor of 0.75. The binary data is first splitThe into information information data segments in of each segment appropriateA is size then modulated using SM In addition, zero- maintaining the same signa and a long tap-dela T obtain the desired transmit power forFrames the are information created sequence suchthan that the frame lengthindoor multiplied by environment. the sampling Thisvalid rate for ensures is the less that frame duration all channel estimations at the receiver are time, i.e., ensure that only a single RF chain is active offset estimation section Experimental Evaluation ofExperimental Evaluation SM (17/31) is 396 samples which , 26100 used is most at format has data frame I16 a signed 16 bit representationsigned 16 ofa an integer number The I16 data format is used which is Each frame has at most 26100 samples - - Digital Signal Processing for Transmission (DSP–Tx) Digital Signal Processing for Transmission A frame includes the frequencydata sequences, offset as estimation shown sequence, below: the pilot and up-sampled The ‘Data section’ is formed from a series of concatenated frames Experimental Evaluation ofExperimental Evaluation SM (18/31) on i 397 mat i est SNR on, i zat i ron h ower in the SNR estimation in the SNR ower p e sync eak and data sections are shown difference in the peak power between the synchronization section and the p pp and data sections Th h i i SNR i i - There is approximately a 21.1 dB - : w ure belo g Digital Signal Processing for Transmission (DSP–Tx) Digital Signal Processing for Transmission In particular, theOffset’ differences estimation between sectionobservable the in and the amplitude the fi of amplitude the of ‘Pilot the and ‘Information Frequency Data’ is clearly Experimental Evaluation ofExperimental Evaluation SM (19/31) 398 having on-board an Intel-i7 processor operating at 1.8 GHz Transmission Hardware (PXIe–Tx) Hardware Transmission NI-PXIe-1075 chassis with 4GB of RAM Experimental Evaluation ofExperimental Evaluation SM (20/31) 399 typical eed p s offset g kHz tunin 10 p eneration g l and l swee na y g si GHz 1 w Q uenc q qy at I/ ske er channe density l l p Generator noise noise differentia Signal phase Transmission Hardware (PXIe–Tx) Hardware Transmission -to-channe , l phase I/Q less than 2 ms fre y dBc/Hz -channel 5450 l s channe - icall dBc/Hz p 160 dBm/Hz average noise density 110 yp y 400 Mega samples (Ms)/s, 16-Bit I/QDua Signal Generator 512 MB of deep on-board memory16-bit resolution 400 Ms/s sampling rate ±0.15 dB flatness to 120 MHz140 with digital flatness correction − 25 − 500 kHz to 6.6 GHz frequencyTyp range PXIe - NI NI-PXIe-5652 RF Signal Generator NI-PXIe-5611 intermediate frequency (IF) to carrier RF up-converter Experimental Evaluation ofExperimental Evaluation SM (21/31) of an 400 with , mapping plane linear a azimuth performs the in receiver dBi generator 7 and signal of I/Q gain transmitter 5450 - the peak a PXIe - zero Transmission Hardware (PXIe–Tx) Hardware Transmission NI between to has the , down antenna separation m particular 2 . The NI-PXIe-5450 I/Qbinary signal file generated generator in Matlab is byIn the fed encoding with DSP–Tx algorithm thethe transmit signed vector 16-bitamplitude from is range the assigned toamplitude to the any output value powerThe equal output to and from 215 polarization, the5652 NI-PXIe-5450 and i.e., I/Q a RF signal peak linearconverter generator signal voltage then scale goes of generator to the theThe which NI-PXIe- NI-PXIe-5611 voltage outputs is theat analogue a connected waveform carrier corresponding frequency to of to theEach 2.3 the GHz binary antenna at data NI-PXIe-5611 theone transmitter half–wave frequency and dipole receiver placed in contains the twoEach middle. quarter-wave All dipoles, three and dipoles areomnidirectional vertically radiation polarized pattern. Theguarantee 10 cm very inter-antenna low, separation if2 is sufficient any, to spatial correlation when broadcasting at 2.3 GHz with a Experimental Evaluation ofExperimental Evaluation SM (22/31) 401 Laboratory Setup Experimental Evaluation ofExperimental Evaluation SM (23/31) 402 having on-board an Intel-i7 processor operating at 1.8 GHz Receiver Hardware (PXIe–Rx) Hardware Receiver NI-PXIe-1075 chassis with 4GB of RAM Experimental Evaluation ofExperimental Evaluation SM (24/31) 403 z MH 4 f o idth w d an bdidth b t a fl t l narea i s lt resu h c hi h clock whi s / s M/ M 10 reference s i Receiver Hardware (PXIe–Rx) Hardware Receiver t -time sampling board l - men it i on srea / 5652 eexper - th n 150 Ms 3 to 250 MHz bandMHz in direct path mode, or 50 MHz bandwidth centered at 187.5 i antennas PXIe - e t NI NI-PXIe-5622 16-Bit Digitizer (I16) NI-PXIe-5601 RF down-converter The receiving antennas are the sameThe as NIPXIe-5601 those used RF for down-converterthe transmission is used to detect theThe analogue signal RF signal isbandpass from then filter with sentra a to real the flat NI-PXIe-5622 bandwidthThe IF equal to digitizer, NI-PXIe-5622 0.4×SampleRate.reference which clock digitizer The and applies writes sampling the is its received binary files own The synchronized recorded binary files with are then the processed according to NI-PXIe-5652 ‘DSP–Rx’ on-board Experimental Evaluation ofExperimental Evaluation SM (25/31) are 404 Rx – PXIe the on digitizer 5622 - PXIe - NI the by recorded files Digital Signal Processing for(DSP–Rx) Reception binary The converted to Matlab vectors In particular, a sample received vector detected by PXIe–Rx on Rx1 is as follows: Experimental Evaluation ofExperimental Evaluation SM (26/31) 405 ) algorithm) on i mat ii i g recovery and correction of each frame est autocorrelation l an anne on hl h g or c f SM maximum-likelihood demodulation ( d (based uence en use q h se st sequence i y l gna il i Digital Signal Processing for(DSP–Rx) Reception ot s il letes the matched-filterin pil p e The Matlab vectors are then combinedThe to form detector a received firstsynchronization matrix finds the beginningThe SNR of is then the calculated using transmittedAfter the the ‘SNR sequence section’ SNR by forits using that underlying vector frames the has beenEach determined, each frame vector iscom is decomposed into then down-sampledThe frequency andfollows offset passed and are estimation, through performed using timin the state-of-the-artTh algorithms RRC filterThe which remaining data, alongestimated with binar the estimated channels, is finally used to recover an Experimental Evaluation ofExperimental Evaluation SM (27/31) t a y 406 emeasuremen CDFs of thecoefficients channel Each is definedRician b with distribution afactor unique K- The markersth denote points while thedenote lines theapproximation best fit Wireless Channel Characterization Channel Wireless Experimental Evaluation ofExperimental Evaluation SM (28/31) 407 The Wireline Test: RF Chain Mismatch The Wireline Test: Experimental Evaluation ofExperimental Evaluation SM (29/31) 408 every of end oint p the R at SN y and beginning the Results at eated 1000 times for ever p estimated information bits is sent per transmission to obtain the 5 is results l eriment is re p channel erimenta p he ex ex Astreamof10 The information data is put inThe 50, 2000 bit, frames frame resulting in 100 channel estimationsT per transmission Experimental Evaluation ofExperimental Evaluation SM (30/31) 409 Experimental Evaluation ofExperimental Evaluation SM (31/31) 410 ) s d ren Td) T n i a Mi M d ts an l Transmitter esu Rl R Networks l the ime-Coded MIMO ca i T at Cellular umer (N i l Information Evaluation of SM l ormance f State -Modulated Space- er y Heterogeneous Pf P erimenta in p rror Introduction and Motivation behind SM-MIMO History of SM Research andTransmitter Research Design Groups – Working Encoding on SM Receiver Design – Demodulation E Achievable Capacity Channel Imperfect Channel State Information at theMultiple Receiver Access Interference Energy Efficiency Transmit-Diversity forSpatiall SM Relay-Aided SM SM SM for Visible Light Communications Ex The Road Ahead – Open ResearchImplementation Challenges/Opportunities Challenges of SM-MIMO . Outline . 1. 2. 3. 4. 5. 6. 7 8. 9. 10. 11. 12. 13. 14 15. 16. 17. 18. : 411 MIMO d ze li Antennas Interference enera Idle Glid G or f the on i of at l Towards u , July 2013 (to appear). [Online]. d o : MdlM i l a i pat Sil S Advantage “ Networks anzo, H Taking . : L d Proceedings of the IEEE ”, Cellular ura, an i ug Si S Harvesting . S , b raye Energy Gh b . A Heterogeneous aas, H . Cell H Frequency ns g enzo, R Appraising theDesi Fundamental Trade-Offs ofLarge-Scale Single- Implementations: Training Overhead vs. for CSIT/CSIR Multi-RF Acquisition MIMO From Single-UserCommunications Point-to-Point toMillimeter-Wave Communications: Multi-User The Need for Beamforming Multi-Cell Gains Small SM–MIMO Engineering Radio Leveraging the Antenna Modulation Principle to a Larger Extent Open Physical-Layer Research Issues Di The Ahead – Road Challenges/Opportunities Research Open . M Challenges, Opportunities and Implementation Available: http://hal.archives-ouvertes.fr/docs/00/84/02/78/PDF/ProcIEEE_SM_FullPaper.pdf. o t 412 y some , eor th x i r ti t .Heremajor However . om ma d MIMO - ran expected d SM antennas) be an y r t can : aware - located - le-RF base stations g found (co cgeome be forwards ti as h can with sin uplink Interference y oc steps thti t s the f to on o ti complexity) significant ca - opportunities li for pp ransmit-diversit T Precoding and CSIT Application etc… Precoding for multi-user SM-MIMO Aliti A (Low etc… the analysis and the design of SM in HCNs Point-to-point SM-MIMOroom has beenimportant studied aspects are extensively still not and completely understood: little Multi-user SM-MIMO andcellular understanding networks have theresearch almost potential been of neglected SM so in far The Ahead – Road Challenges/Opportunities Research Open 413 … unknown timization: is p diversity assessment and o y achievable end : - to - efficienc c… t gy Advantages and disadvantages against state-of-the-art relaying End Error propagation and related low-complexity receivere design The number of RFoff chains vs. is the still total unclear number ofFair performance antennas trade- assessmentthe-art and optimization against state-of- Realistic/fair comparison with massive MIMO etc… Distributed SM-MIMOunexplored for uplink applications is still almost Ener Testbed/practical implementation and measurements The Ahead – Road Challenges/Opportunities Research Open c ti 414 filters romagne t ec shaping ltl ti e d orthogonal on an ti if a t emen errors l mp ili tti on i y zat i antization u qantization q MIMO aracter enna-arra - t hii h and SM oss c l ean time l ng carrier - hi tc … i arge-sca w compatibility assessment Antenna switching at the symbol time Sihi S Reconfigurable single-RF antennasignatures design to createBandwidth unique efficient channel finite-duration pulse shaping L Multi Efficient channel estimation with single-RF transmitters Sampling are used etc Implementation Challenges of SM-MIMO 415 ) ) 264759 rant 317126 ireless Mobile Communications g grant W ect, j ro p pj project, GREENET - ITN-CROSSFIRE (ITN ( Union ean Union p European M. Di Renzo Haas H. A. Ghrayeb he UK-China Science Bridges: R&D on (B)4G he Euro The The Engineering and Physical Sciences ResearchThe Council Laboratory (EPSRC), UK of Signals and SystemsT (“Jeunes Chercheurs 2010”), France The Italian Inter-University Consortium for TelecommunicationsT (CNIT), Italy EADS Deutschland GmbH, Germany We gratefully the support acknowledge of: We Thank You for Your Attention for Your Thank You t hif 416 state fading . space s 2009 f channel Rician ly detection and u l Jl J so i , ys li l partial 3703 - correlated with 3692 p. over p , 7 ormance ana f er p MIMO ,no. modulation 8 ) . - or l f o v k (SSK) (SSK ramewor ommun., keying keying fk f C l ess shift shift l re genera Wi l Space “A “Space “ , , rans. aas, T H Haas Haas . . . H H H IEEE , d . Ghrayeb, and L. Szczecinski, “Spatial modulation: Optima ” s A l and and . anne 2010 enzo an hl” h c R Renzo Renzo June . Di Di Di , . . . performance analysis”, IEEE Commun. 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