Advances in Coordinated Multi-Cell Multi-User MIMO Systems

Li-Chun Wang 王蒞君 Department of Electrical Engineering National Chiao Tung University Hsinchu, Taiwan http://lichun.cm.nctu.edu.tw [email protected]

1 Information for presentation slides

• http://www.ieee-globecom.org/2011/private

2 Outline 1 Introduction to MIMO Antenna Techniques 2 Single-cell Multi-user MIMO Systems 3 Single-cell Multi-user MIMO Broadcast Systems 4 Multi-cell Network MIMO systems 5 Updates of Network MIMO Techniques in 3GPP LTE-A Updates 6 Conclusions Main Story

• Story 1: Scheduling in diversity-based MIMO • Story 2: Scheduling for multiplexing-based MIMO • Story 3: A robust multi-user MIMO broadcast system against huge feedback channel variations • Story 4: Channel assignment for multi-cell Network MIMO • Story 5: Viewpoints of 3GPP on network MIMO What happened in 1895? 1895 in Taiwan 1895 in Italy

It is dangerous to put limits on wireless! (1932) Guglielmo Marconi (1874 ~ 1937)

• In 1895 he began laboratory experiments and succeeded in sending wireless signals over a distance of one and a half miles. • In 1896, he was granted the world's first patent for a system of wireless telegraphy. • On 12 December 1901, using a 150 meter (500ft) kite- supported antenna for reception, the message fwas received at Signal Hill in Newfoundland of Canada by the company's new high-power station at Poldhu, which was transmitted about 3,500 kilometres (2,200 mi). • Guglielmo Marconi and Ferdinand Braun won the Nobel Prize in Physics 1909.

8 Wireless 101

The more things changes, the more remain the same. (Alphonse Karr, 1808) What is the fundamental issue for wireless communications?

Spectrum Efficiency Some Milestones in Telecommunications

• Fundamental resources (degrees of freedom) in communication system engineering – Power (pre-1948), – Bandwidth (1948, Shannon Capacity) – complexity (1980, TCM), – Space (1998, Space-Times Processing )

11 An Open Issue

What is the next possible degree of freedom that can be exploited for communications systems?

12 Research Trends in Wireless Networks • The Past Two Decades: Key Developments at the Link Level : – MIMO; MUD; Turbo • Today: An Increased Focus on Interactions Among Nodes – Competition » Cognitive radio » Information theoretic security » Game theoretic modeling, analysis & design – Collaboration » Network coding » Cooperative transmission & relaying » Multi-hop transmission & coalition games » Collaborative beam-forming » Collaborative inference – Competition & Collaboration in Wireless Networks Hint from this Talk

How can we effectively exploit the degree of freedom in the user domain to design multiuser MIMO and multi-cell network MIMO systems?

14 1. Introduction to MIMO Antenna Techniques

(Story 1: Scheduling in diversity-based MIMO)

15 Multi-Input Multi-Output (MIMO) Antenna Techniques

• Independent parallel transmit and receive antenna pair • MIMO antenna techniques – boost channel capacity – enhance link reliability – reject strong interference

Nt Nr

Tx … … Rx

16 Diversity-Multiplexing Tradeoff

• Diversity -> improve link reliability by replicas

a a a a Tx Rx a a

• Multuplexing -> enhance data rate by multiplexing

a a+b ba Tx Rx ba b b+a

subchannel interference canceling is required 17 Scheduling Techniques

• Through periodically selecting the best user to serve, the system performance is improved by exploiting multiuser diversity or cooperative diversity. • Ordered Statistics is the fundamental mathematical techniques for analyzing the scheduling wireless systems.

user 1 user 1 user 2 user 3

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 18 A Multiuser MIMO Scheduling System

• Perfect SNR estimation and noiseless feedback (k) (k) • Hk=[ hij ], each hij is subject to Nakagami fading • The BS selects the target user with highest effective SNR

19 The Benefit from Multiuser Scheduling

multiuser diversity or cooperative diversity

20 Nakagami-m Fading Channel

Which one delivers the highest capacity ?

21 Impact of Channel Fading on System Capacity

m=1

Two totally different stories for K=1 and K >1

22 A Multiuser MIMO Scheduling System

• Perfect SNR estimation and noiseless feedback (k) (k) • Hk=[ hij ], each hij is subject to Nakagami fading • The BS selects the target user with highest effective SNR

23 A Generic Diversity-Based MIMO System

The maximum diversity gain = Nt Nr

24 Some Diversity-Based MIMO Schemes

• Four existing diversity-based MIMO schemes are considered – ST/SC ST : selective transmission – ST/MRC SC : selective combining MRT : maximum ratio transmission – MRT/MRC MRC : maximum ratio combining – STBC STBC : space time block code

• All of them can deliver full antenna diversity gain

25 When Diversity-Based MIMO Meets Multiuser Scheduling • In general, scheduling is a MAC layer technique to deliver multiuser diversity gain by exploiting independent channel fluctuations among users • By contrast, antenna diversity is a physical layer approach to offer reliable transmissions with the major goal of mitigating channel fading

Induced link variation by diversity-based MIMO

SISO

26 A Closer Look for their Interplay

Diversity-based “peak” MIMO Link

Array gain

SISO

27 An Analytical Upper Bound

• In [Chen & Wang ’04], we show that system capacity selection order AF gain

array gain SNR Nakagami fading parameter

28 ST/SC over Multiuser Scheduling System

SISO

ST/SC virtual users virtual antennas MS 1 MS 1 MS 4

Rx- Tx MS 2 MS 2 MS 5 SC

MS 3 MS 3 MS6

K=3, Nt=1, Nr=2 K=6, Nt=1, Nr=1 K=1, Nt=1, Nr=6

29 MRT/MRC over Multiuser Scheduling System

MRC SISO SC MRC

SISO

30 STBC over Multiuser Scheduling System

STBC (Nt,1)

SISO

STBC channel damping

MS 1 Fading Fading MS 1 Fading MS 1 channel = channel < channel m 4*m m MS 2 MS 2 MS 2

31 System Capacity with Joint Antenna and Multiuser Diversity

32 Main Point in Story 1

• Multiuser scheduling and the diversity-based MIMO with the multiuser scheduling system may not be a good marriage. • Why? – User population, i.e. K, has contributed a large amount of diversity in the whole system – More important is their intrinsic conflicts – one prefers variation, while the other creates tranquility

33 2. Single-cell Multi-user MIMO Systems

(Story 2: Scheduling for multiplexing-based MIMO)

34 Issue for a Spatial Multiplexing-based MIMO

• Diversity-multiplexing tradeoff in a point-to-point MIMO system – multiplexing gain comes at the price of diversity gain [Zheng & Tse ’03] – may translate into smaller coverage areas if the SM MIMO scheme is used [Catreux & Greenstein ‘03]

• The coverage is defined as the maximum distance at which

the link suffices for maintaining a required receive SNR γth with a probability, say 90%, at least 35 Scheduling Techniques

user 1 user 1 user 2 user 3

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 36 A Multiuser MIMO Scheduling System

• Perfect SNR estimation and noiseless feedback (k) (k) • Hk=[ hij ], each hij is subject to Nakagami fading • The BS selects the target user with highest effective SNR

37 The SWNSF Scheduling Game

SWNSF: strongest-weakest- normalized-subchannel-first MS 1

MS 2

MS 3

38 Effect of SWNSF Scheduling on λmin

SWNSF scheduling enhances the output SNR of the weakest subchannel

λ3 > λ2 >λ1

39 s u Effect of SWNSF Scheduling on Coverage i d a r e g 1.8 a r e 1.6 N=2 v o 1.4 c N=3 MS MS d e 1.2 z i l 1 a m MIMO + RR Scheduling r 0.8 o N 0.6 MIMO + SWNSF Scheduling (“soft” coverage depends on K)

0.4 1 5 10 15 20 25 30 Number of users K 40 Effect of SWNSF Scheduling on Otherλ i

42 Coverage Extension & Capacity Improvement

number of users

number of antennas

43 Main Point in Story 2

Multi-User Scheduling is A Soft Coverage Extension Technique

"Enhancing Coverage and Capacity for Multiuser MIMO Systems by Utilizing Scheduling, " IEEE Trans. on Wireless Communications, Vol. 5, No. 5, pp. 1148-1157, May, 2006. 3. Single-cell Multi-user MIMO Broadcast Systems

Story 3: A robust multi-user MIMO broadcast system against huge feedback channel variations

45 MIMO Broadcast Systems

• MIMO antenna in point-to-point scenario can boost capacity through spatial multiplexing.

• MIMO broadcast systems (point-to-multiple) can transmit personalized data services to multiple users concurrently.

Traditional point-to-point transmission

Point-to-multiple transmissions in spatial domain: Multi-User MIMO broadcast systems

46 Challenges in Transmit Beamforming MU-MIMO • Types of MU-MIMO Transmit beamforming systems: – Zero-forcing (ZF) based transmit beamforming – Block diagonalization (BD) based transmit beamforming – Dirty paper coding (DPC)

• Challenges of feedback channel design in transmit beamforming MU-MIMO broadcast » Accuracy » Bandwidth => codebook solution

47 MIMO Broadcast Systems with Transmit Beamforming

Receive H1 Beamforming

Transmit Beamforming

W HK Receive Beamforming

MTMR complex-valued entries of channel matrix per user

• Utilize feedback information to calculate the beamforming weight W for interference cancellation • ZF-DPC: QR-decomposition based transmit beamforming • ZF: channel inverse based transmit beamforming MIMO Broadcast Systems with Joint Transmit Precoding and Receive Equalizer

Receive H1 Equalizer R Transmit 1 Precoding

T HK k Receive Equalizer

Hk RK

Rk MTMR complex-valued entries of channel matrix per user

Need return Rk to each user!!

• Utilize feedback information to calculate Tk and Rk for interference cancellation – Block diagonalization (BD) A Nonconformist (Why Not) Issue

• Most MU-MIMO systems design transmit beamforming (precoding) at the transmitter side to cancel the inter-stream interference among serving users, but suffer from feedback channel errors and bandwidth issues.

• Q. Can we have a different architecture to realize MU- MIMO broadcast systems?

50 Our Answer

• Receive ZF beamforming MIMO broadcast systems combined with multiuser scheduling • An alternative MU-MIMO technique to use feedback channel for a different purpose => user selection. • Has advantages in terms of robustness against CSI and feedback link errors [Wang’10]

[Wang’10] Li-Chun Wang and Chu-Jung Yeh, "Scheduling for multiuser MIMO broadcast systems: transmit or receive beamforming?" IEEE Transactions on Wireless Communications, vol. 9, no. 9, pp. 2779 – 2791, Sep, 2010.

51 New Perspective: MIMO Broadcast Systems with Receive Beamforming

Receive H1 Beamforming W Transmit 1 Beamforming

HK Receive Beamforming [γ1, γ2, … ] WK MT SNR values

MT real-valued scalars per user (vector feedback)

• Utilize feedback information to select the appropriate users • The receive ZF MIMO broadcast systems: channel inverse based receive beamforming Example of Receive ZF Beamforming (3 x 3) • Feedback information – User 1: [1.25, 0.49, 0.50] – User 2: [0.81, 0.70, 2.25] – User 3: [0.49, 1.69, 1.21] (Vector feedback)

Antenna 1 Antenna 2 Antenna 3 Select User 1 User 3 User 2

53 BD: Block diagonalization Literature Survey on MU-MIMO

Tx BD Rx Note

[Caire’03] o x x Concept of ZF-DPC and ZF beamforming [Jindal’05] [Sharif’07] o x x The gain of broadcast system over point-to-point transmission [Vishwanath’03] o x x Theoretical capacity region of MIMO broadcast [Viswanath’03] systems [Weingarten‘06] [Tu’03][Dimi´c’05] o x x Scheduling algorithms and sum-rate performance [Yoo’06][Bayesteh’08] evaluation and analysis [Jindal’06][Yoo’07] o x x Under limited feedback condition: sum-rate analysis, [Swannack‘06] codebook and scheduling algorithm design [Caire’10] [Ding’07] [Zhang’09] [Spencer’04][Chen’08] x o x The concept, theoretical capacity, scheduling [Shen’06,07] algorithms of BD [Heath’01][Airy’04] x x o Concept. scaling law. sum-rate analysis under equal [Chen’07] power allocation (MR = MT case) Our work in [Wang’10] o o ◎ Effects of feedback channel variations (Part I) [Wang’11a] Link performance analysis (Part 2) 54 [Wang’11b] Effects of channel estimation error for receive ZF beamforming (Part 3) Key Analytic Results (MR = MT = M) • The sum rate of the receive ZF MIMO broadcast systems with water-filling power allocation

The water-level solution of the equation

– Utilize order statistics technique and long-term power constraint for water-filling equation to derive the closed form

ΓR(a,x): upper incomplete gamma function 55

Ei(x): exponential integer function of order i Example of an MU-MIMO System with Receive ZF Beamforming (3 x 3) • Feedback information – User 1: [1.25, 0.49, 0.50] – User 2: [0.81, 0.70, 2.25] – User 3: [0.49, 1.69, 1.21] (Vector feedback)

Antenna 1 Antenna 2 Antenna 3 Select User 1 User 3 User 2

56 Key Analytic Results (MR ¸ MT) • A general form of the sum rate with water-filling

power allocation L = MR – MT

Key steps in the closed form The water-level solution of the equation • Order statistics technique • Long-term power constraint for water-filling equation • The integral identity provided in [Alouini’99]

57 Sum Rate Comparison

• Under similar feedback requirement

MT values for feedback!! Transmit beamforming: MT = {2, 4}; MR =1 Receive beamforming: MT = {2, 4} = MR

RZFS≒TZFS in sum rate

58 What happen if feedback information is perturbed or channel estimation is inaccurate?

59 Two Issues on channel information accuracy

• Can receive beamforming ZF MIMO broadcast systems tolerate feedback channel variations? • Can receive beamforming ZF MIMO broadcast systems channel estimation errors?

Receive H1 Beamforming

Transmit Beamforming channel estimation errors

HK Receive Beamforming [Wang’07] each entry of [γ1, γ2, … ] Ek is distributed by

feedback channel variations (Part I-1)

60 Feedback CSI Variations

• Adopt coefficient of variation (CV) to evaluate the impact of feedback CSI variations – Perturbed from the noisy estimated information, the outdated information, and quantization errors, etc.

– For random variable X, CV = σx / E[X]

• For the feedback CSI x, the perturbed feedback CSI received at BS is x’ = x + △

2 – △ ~ N(0, σx ) and σx = CV．x

61 Effects of Feedback CSI Variations on Sum Rate (CV = 0.5) K = 20, M = M = 3 T R • With perfect CSI feedback – transmit beamforming is better than receive beamforming (1 ~ 1.5 nats/s/Hz)

– ZF-DPC is the best one

• With perturbed CSI feedback – Large sum rate degradation for transmit beamforming (Reduce 19.7% ~ 33.8%)

– Slight sum rate degradation for receive ZF beamforming (Reduce 3.5%)

62 Effects of Feedback CSI Variations on Sum Rate (CV = 1.5)

K = 20, MT = MR = 3 • Receive ZF beamforming is more robust to feedback CSI variations – 7% sum rate reduction – Keep the sum rate slope

• Transmit beamforming is more sensitive to feedback CSI variations – 38.8 ~ 61.1% sum rate reduction – Interference dominated sum rate performance

63 Summary

• The MU-MIMO with receive beamforming combined with multiuser is still an interesting alternative MU-MIMO subject to feedback channel errors.

• Imperfect CST at the receiver (CRI-R) will cause serious sum-rate performance degradation and cause the sum-rate floor. – Sum rate capacity will no longer linearly increase with SNR in decibel and be bounded.

64 Main Point in Story 3

Multi-user broadcast MIMO systems DO NOT require transmit beamforming precoders.

“Scheduling for Multiuser MIMO Broadcast Systems: Transmit or Receive Beamforming?” IEEE Trans. on Wireless Communications, Vol. 9, No. 9. pp. 2779~2791, Sep. 2010. 4. Multi-cell Network MIMO systems

Story 4: Channel assignment for multi-cell Network MIMO

66 What and Why Network MIMO? • Inter-cell interference: In conventional cellular systems, the solution is using larger frequency reuse among cells – Poorer spectral efficiency!! Inter-stream interference free!!

MIMO broadcast system

CSI exchange through high-speed backbaul Inter-cell interference free!!

Network MIMO system 67 The concept is also used in 3GPP LTE-A (CoMP) and IEEE 802.16m (Co-MIMO) key Issues for Network MIMO

2nd-tier neighboring cells

IGI causes serious signal 1st-tier neighboring cells quality degradation

14 dB degradation for 7-cell network68 MIMO Conventional omni-cell approach 23 dB degradation for 3-cell network MIMO Literature Survey

FFR? Sector or Network Note omni-cell? MIMO?

[Lei’07], o sector x SINR improvement and soft [Fujii’08] handoff scheme [Chiu’08] [Shamai’01] x omni o The concept of multi-base [Karakayali’06] station cooperation

[Somekh’06] x Wyner’s o Performance analysis based [Jing’07] model on Wyner’s circular cell model [Zhang’09] x omni o BD-based network MIMO performance

[Boccardi’07] x sector o Effect of network MIMO [Huang’09] + 3, 6, 12 sector per cell

Our work o sector o Design a 3-cell network MIMO in FFR tri-sector cell 69 Basic Fractional Frequency Reuse (FFR) Concept

70 FFR in Tri-Sector Cell* (Regular Approach)

Each cell has the same frequency partition!!

71 *F. Khan, LTE for 4G Mobile Broadband: Air Interface Technologies and Performance, 1st ed. Cambridge University Press, 2009. Proposed Solution

Coordinated group under certain frequency band

72 FFR: fractional frequency reuse Regular versus Rearranged Frequency Partitions A cell can simultaneously forms 3 groups with neighboring 6 cells

73 Benefit of Rearranged Frequency Partition

• Extra 8.5 dB SINR improvement!! (compared to traditional tri-sector FFR)

10 dB improvement for sectoring FFR

Extra 2.7 dB gain under regular partition + 3-cell network MIMO

Extra 8.5 dB gain under rearranged partition + 3-cell network MIMO

90% percentile

74 Proposed Architecture with 60o Sectoring

75 Benefit of Proposed Architecture over Conventional Approach • A smaller coordination, 3-cell network MIMO architecture, can even outperform conventional 7-cell network MIMO system – 2 dB SINR improvement for 60o sectoring – 3 dB SINR improvement for 120o sectoring

Traditional 7-cell approach Proposed 3-cell approach with rearranged partition

76 Benefit of Multiple Antennas at the Base Station

• Each sector BS equips with t transmit antennas to serve t users simultaneously. • 3-cell network coordination with proposed rearranged tri- sector frequency partition. – Total 3t transmit antennas serve 3t user terminals

Almost linear sum rate improvement as number of antennas t increases

Proposed network MIMO architecture achieves extra gain over conventional approach

77 Summary

• By exploiting geographic cell distribution and frequency partition, a 3-cell network MIMO can outperform conventional 7-cell network MIMO with omni-directional cell

• This kind of 3-cell coordinated network MIMO architectures is particularly useful – The default number of neighboring cells in the Co-MIMO transmission is three for IEEE 802.16m – In LTE-A RAN 1 meeting, the number of coordination in CoMP is also three

CoMP: coordinated multi-point CoMIMO: collaborative multiple-input multiple-output

78 Main Point in Story 4

Network MIMO systems only need 3 cell site to cooperate

• Li-Chun Wang and Chu-Jung Yeh, “3-Cell network MIMO architectures with sectorization and fractional frequency reuse,” IEEE Journal in Selected Area in Communications, Vol. 29, No. 6, pp. 1185~1199, June, 2011 • Li-Chun Wang and Chu-Jung Yeh, “Antenna architectures for network MIMO,” Cooperative Cellular Wireless Networks,” Cambridge University Press, ISBN-13: 9780521767125.

• IEEE C802.16m-09/2280 “Frequency Planning for Inter-Cell Interference Reduction in 3-Cell Collaborative MIMO Systems”

79 5. Updates of Network MIMO Techniques in 3GPP LTE-A Updates

80 3GPP LTE/HSPA Evolution HSPA Series Evolution (CDMA-based) • DL: 2x2 MIMO+64QAM • DL: DC+64QAM+MIMO • DL: 2x2 MIMO or DC+64QAM • UL: DC • DL: 4-Carrier

LTE Series Evolution (OFDM-based) • DL: 4x4 MIMO in 20MHz 3GPP Concept for IMT-A (4G)

Note: Dates refer to the first completed (full) specifications 81 LTE-A Key Technologies

• Carrier Aggregation – Aggregate bandwidth up to 100MHz • Downlink transmission scheme – Improvements to LTE by using 8x8 MIMO – Data rates of 100Mb/s with high mobility and 1Gb/s with low mobility • Uplink transmission scheme – Improvements to LTE by using 4x4 MIMO – Data rates up to 500Mb/s • Relay Functionality – Improving cell edge coverage – More efficient coverage in rural areas

82 Advanced MIMO Techniques in DL

• Extension up to 8-stream transmission – Increased from 4 streams in Rel-8/9 – Satisfy peak SE requirement (i.e., 30 bps/ Hz) • Support for enhanced MU-MIMO – Not more than 4 UEs are co-scheduled – Not more than 2 layers per UE

83 Advanced MIMO Techniques in UL

• Introduction of SU-MIMO up to 4-stream transmission – Rel. 8 LTE does not support SU-MIMO – Satisfy peak SE requirement (i.e., 15 bps/ Hz) • Introduction of UL transmit diversity for PUCCH – Improved signaling robustness and cell- edge performance

84 Enhanced ICIC

• Enhanced ICIC for non-CA based deployments of heterogeneous networks for LTE – WI proposed by CMCC in RAN#47 (Mar. 2010) – Identify and evaluate non-CA based ICIC strategies of heterogeneous network deployments – HetNet use cases are priorities as follows: » Indoor HeNB clusters » Outdoor Hotzone cells » Indoor Hotzone scenarios

85 R10 LTE-A Performance Improvement

• Rel-8 LTE vs. IMT-A requirements – DL: Rel-8 LTE fulfills IMT-A requirements – UL: Need to double from Rel-8 to satisfy IMT-A requirement FDD Rel-8 LTE(1) Rel-10LTE-A(2) IMT-Advanced Peak spectrum efficiency DL 16.3 30.6 15 [bps/Hz] UL 4.3 16.8 6.75

TDD Rel-8 LTE(1) Rel-10LTE-A(2) IMT-Advanced Peak spectrum efficiency DL 16 30 15 [bps/Hz] UL 4 16.1 6.75 Note: Source: 3GPP TR 36.912 (1)4x4 MIMO in DL, SIMO in UL (2)8x8 MIMO in DL, 4x4 MIMO in UL

86 R11 LTE-A New Technical Issues

• CoMP (Coordinated Multipoint Transmission and Reception) • MTC (Machine-Type Communications ) • Network Energy Saving • MODAI (Mobile Data Impact)

87 CoMP (Coordinated Multipoint Transmission and Reception) • New study Item in Rel-10 – Approved in RAN#47 (Mar. 2010) • DL CoMP Transmission Scheems – Joint Processing » Joint transmission/dynamic cell selection – Coordinated Scheduling/Beamforming • Better cell edge performance – Less co-channel interference / more signal

88 Joint Processing (JP)

The coordinated cells will serve the user together. The base station will exchange the channel state information (CSI) and data to each other.

CSI and data exchange between each base station

Coordinated transmission to the user Coordinated Beamforming (CB)

If there are two users close to each other but in different cells. By setting the appropriate beaming weight, we can decrease the interference from different base stations.

Only CSI exchange between each base station

Only one base station will transmit to the user Coordinated Scheduling (CS)

Use the coordinated scheduling to decide which user will be serve. Even without interference nulling, we can avoid the interference from the other base stations.

Only CSI exchange between each base station

Only one base station will transmit to the appropriate user Mulit-Cell Co-operations

JP CB CS Joint Processing ☺ X X for User Data Shared CSI in ☺ ☺ ☺ Multi-Sites Joint group 1 1 DataTransmission Interference ☺ ☺ X Nulling Beamforming ☺ ☺ ☺

JP : Joint Processing CB : Coordinated Beamforming CS : Coordinated Scheduling 4 Scenarios for CoMP in 3GPP LTE-A

• According to R1-110564 in 3GPP, CoMP (coordinated multipoint transmission) techniques are applied in the following 4 scenario

Scenario 3 : Heterogeneous network with low power Scenario 1 : Homogeneous network with intra-site RRHs, which create cells with their own cell ID.

Scenario 2 : Homogeneous network with high Tx power Scenario 4 : Heterogeneous network with low power RRHs RRHs, which create cells with the same cell ID as the macro. NCTU has built a LTE-A System-Level Simulator

• In order to propose new techniques for LTE-A systems, we must establish the LTE-A system-level simulator and calibrate the performance metrics in 3GPP TR36.814. Step(1a) : wideband Path-loss, Shadowing, Antenna Pattern SINR

Step(1c) : DL SINR and cell Spatial Channel Model, OFDMA, MRC Antenna spectral efficiency Combining, Adaptive MCS , HARQ

Codebook-based Precoder, MMSE receiver, Rank SU/MU-MIMO Adaptation, SU/MU Mode Switching

Hierarchical Network RRH Selection, Cooperation/Non-cooperation MIMO mode switching 94 MIMO Physical Layer Simulation Flow Chart

95 NCTU LTE-A System-Level Simulator Interface

96 Hierarchical Base Station Cooperation with Single Cell ID (HBSC-S)

• If the RRH nodes share the same cell ID with the corresponding macro-BS, the RRH nodes can be regarded as the distributed antennas of the macro-BS. We call the system as Hierarchical Base Station Cooperation with the Single cell ID (HBSC-S).

97 Hierarchical Base Station Cooperation with Multiple Cell IDs (HBSC-M)

• We apply the cooperation technique to mitigate the intra-cell interference. • We call the system as Hierarchical Base Station Cooperation with Multiple cell IDs (HBSC-M).

98 Wideband SINR for Calibration Step 1a

 Our calibration result is consistent with those in 3GPP.

99 Spectral Efficiency for Calibration Step 1c

 Our calibration result is consistent with those in 3GPP.

100 Spectral Efficiency for MIMO Systems

• All our simulation results are consistent with those in 3GPP.

Spectral Min-value of Our Work Max-value of Efficiency others in 3GPP (bits/s/Hz) others in 3GPP (bits/s/Hz) (bits/s/Hz) 2x2 SU-MIMO 2.14 2.37 2.47 Cell-Average 2x2 SU-MIMO 0.072 0.079 0.100 5% Cell-Edge 4x2 SU-MIMO 2.34 2.52 2.66 Cell Average 4x2 SU-MIMO 0.085 0.089 0.110 5% Cell-Edge 2x2 MU-MIMO 2.56 2.62 2.77 Cell-Average 2x2 MU-MIMO 0.070 0.086 0.110 5% Cell-Edge

101 Spectral Efficiency for HBSC-M Systems in Multi-Cell Case

• HBSC-M systems outperform both the HBSC-S systems and the SU- MIMO system in the spectral efficiency. • The best position for each RRH node is 0.6R~0.7R.

+12%

+ 20%

102 Energy Efficiency for HBSC Systems

• When considering the energy efficiency (bits/ Joule), the HBSC-S system with 1 RRH selected outperforms the HBSC-M system.

+ 9%

103 Main Point in Story 5

LTE-A system-level simulator supporting hierarchical network MIMO is important.

3GPP R1-111528, “Simulation Evaluation for CoMP Scenarios 3 and 4”, May 2011

“Performance Calibration and Simulation Methodology for 3GPP LTE-A Systems.” IEEE VTS APWCS, Aug. 2011

2011/7 Conclusions

• We have discussed multiuser MIMO communications systems from the perspective of radio resource arrangement and place emphasis on user scheduling and channel assignment correctly. • Main point: If we utilize the degree of freedom in the user domain cleverly, we can deliver many advantages to MU- MIMO and network MIMO: – enhancing coverage and capacity simultaneously; – Improving robustness to channel variations; – Reducing the necessity of large number of cooperative cell site.

105 Future Research Directions

• Channel state feedback for downlink network MIMO

• Asynchronous uplink network MIMO

• Hierarchical network MIMO

106 Final remark

Coming together is a beginning. Keeping together is progress. Working together is success.

~ Henry Ford

107 References

• C. J. Chen and Li-Chun Wang, "Enhancing Coverage and Capacity for Multiuser MIMO Systems by Utilizing Scheduling, ” IEEE Trans. on Wireless Communications, Vol. 5, No. 5, pp. 1148-1157, May, 2006. • C. J. Chen and Li-Chun Wang, “A Unified Capacity Analysis for Wireless Systems with Joint Multiuser Scheduling and Antenna Diversity in Nakagami Fading Channels,” IEEE Trans. on Communications, vol. 54, No. 3, pp. 469~478, Mar. 2006. • Li-Chun Wang and Chu-Jung Yeh, “Scheduling for Multiuser MIMO Broadcast Systems: Transmit or Receive Beamforming?” IEEE Trans. on Wireless Communications, Vol. 9, No. 9. pp. 2779~2791, Sep. 2010. • Li-Chun Wang and Chu-Jung Yeh, “3-Cell network MIMO architectures with sectorization and fractional frequency reuse,” IEEE Journal in Selected Area in Communications, Vol. 29, No. 6, pp. 1185~1199, June, 2011 • Li-Chun Wang and Chu-Jung Yeh, “Antenna architectures for network MIMO,” Cooperative Cellular Wireless Networks,” Cambridge University Press, ISBN-13: 9780521767125. • IEEE C802.16m-09/2280 “Frequency Planning for Inter-Cell Interference Reduction in 3-Cell Collaborative MIMO Systems”

108 References

• Y. J. Liu, T. T. Chiang and Li-Chun Wang “Performance Calibration and Simulation Methodology for 3GPP LTE-A Systems.” IEEE VTS APWCS, Aug. 2011 • 3GPP R1-111528, “Simulation Evaluation for CoMP Scenarios 3 and 4”,May 2011

• ------

109 Reference

[1] TR 36.814 : 3rd Generation Partnership Project ; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E- UTRA); Further advancements for E-UTRA physical layer aspects (Release 9) [2] TR 36.819 : 3rd Generation Partnership Project;Technical Specification Group Radio Access Network; Coordinated multi-point operation for LTE physical layer aspects(Release 11) [3] M.2135 Report ITU-R M.2135-1(12/2009) Guidelines for evaluation of radio interface technologies for IMT-Advanced M Series Mobile, radiodetermination, amateur and related satellites services [4] S. Shamai and B. M. Zaidel, “Enhancing the cellular downlink capacity via co-processing at the transmitting end,” IEEE Vehicular Technology Conference Spring, vol. 3, pp. 1745 – 1749, May 2001. [5] H. Zhang and H. Dai, “Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2004, no. 2, pp. 222–235, 2004.

110 Reference

[6] G. J. Foschini, K. Karakayali, and R. A. Valenzuela, “Coordinating multiple antenna cellular networks to achieve enormous spectral efficiency,” IEE Proceedings-Communications, vol. 153, no. 4, pp. 548 – 555, Aug. 2006. [7] S. Jing, D. N. C. Tse, J. B. Soriaga, J. Hou, J. E. Smee, and R. Padovani, “Multicell downlink capacity with coordinated processing,” EURASIP Journal on Wireless Communications and Networking, 2008. [8] H. Huang, M. Trivellato, A. Hottinen, M. Shaﬁ, P. Smith, and R. Valenzuela, “Increasing downlink cellular throughput with limited network MIMO coordination,” IEEE Transactions on Wireless Communications, vol. 8, no. 6, Jun. 2009. [9] F. Richter, A. J. Fehske, and G. Fettweis, “Energy efficiency aspects of base station deployment strategies for cellular networks,” IEEE Vehicular Technology Conference Fall, Sep. 2009. [10] A. J. Fehske, P. Marsch, and G. P. Fettweis, “Bit per Joule efficiency of cooperating base stations in cellular networks,” IEEE GLOBECOM Workshops, Dec. 2010.

111 Reference

[11] J. Salo, G. Del Galdo, J. Salmi, P. Kyosti, M. Milojevic, D. Laselva, and C. Schneider. (2005, Jan.) MATLAB implementation of the 3GPP Spatial Channel Model (3GPP TR 25.996). [Online]. Available: http://www.tkk.ﬁ/ Units/Radio/scm [12] 3GPP, R1-110867, “CoMP with lower Tx power RRH in heterogeneous network,” NTT DOCOMO, Feb. 21-25, 2011. [13] L. Thiele, T. Wirth, M. Schellmann, Y. Hadisusanto, and V. Jungnickel, “MU- MIMO with localized downlink base station cooperation and downtilted antennas,” IEEE International Conference on Communications Workshops, pp. 1–5, 2009. [14] N. Jindal, “Antenna combining for the MIMO downlink channel,” IEEE Transactions on Wireless Communications, vol. 7, no. 10, pp. 3834–3844, Oct. 2008. [15] S. Fang, G. Wu, Y. Xiao, and S. Q. Li, “Multi-user MIMO linear precoding with grassmannian codebook,” International Conference on Communications and Mobile Computing, vol. 1, pp. 250–255, 2009

112 Reference

[16]. M. Sawahashi, Y. Kishiyama, A. Morimoto, D. Nishikawa, and M. Tanno, “Coordinated multipoint transmission/ reception techniques for LTE- Advanced,” IEEE Wireless Commun. Mag., pp. 26-34, June 2010. [17]. S. A. Jafar, “Interference Alignment: A New Look at Signal Dimensions in a Communication Network,” Foundations and Trends in Communications and Information Theory, 2011.

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