DOCSLIB.ORG

A Symbol Rate Multi-User Downlink Beamforming Approach for Wcdma

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Symbol Rate Multi-User Downlink Beamforming Approach for Wcdma A SYMBOL RATE MULTI-USER DOWNLINK BEAMFORMING APPROACH FOR WCDMA Michael Joham, Wolfgang Utschick, and Josef A. Nossek 10th IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC’99) Osaka, Japan volume 2, pp. 228–232, September 12th–15th, 1999 c 2005 IEEE. Personal use of this material is permitted. However, per- mission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Munich University of Technology Institute for Circuit Theory and Signal Processing http://www.nws.ei.tum.de A SYMBOL RATE MULTI-USER DOWNLINK BEAMFORMING APPROACH FOR WCDMA Michael Joham, Wolfgang Utschick and Josef A. Nossek Institute for Circuit Theory and Signal Processing Munich University of Technology Arcisstr. 21, 80333 Munich, Germany [email protected] (m) Abstract - The downlink spectral efficiency of 3rd gener- where sk 2 {−1; +1g denotes the BPSK modulated ation mobile radio systems will be increasingly important (m) symbols. The spreading code ck (t) for the m-th symbol due to the asymmetric features of several services. On (m) of mobile k is composed of Q chips d [q] 2 {−1; +1g the average, the downlink data rates will be higher than k k and is of length T = Q T . In WCDMA the chiprate on the uplink. We propose to utilize adaptive antennas k k c equals 1=T = 4:096 Mchips/s. The chip-waveform at the base stations because spatial interference suppres- c p (t) has a square-root raised cosine spectrum with a sion is able to reduce the near-far effect in the downlink rrc rolloff factor of . Note that the chips (m) of single-user detection DS-CDMA systems. The algo- α = 0:22 dk [q] rithm that calculates the downlink beamforming vectors are changing from symbol to symbol because they are de- takes into account the correlation properties of the spread- fined as the multiplication of the q-th chip of the Orthogo- ing and scrambling codes. We presume an estimation of nal Variable Spreading Factor (OVSF) code belonging to the downlink channel parameters in terms of the directions mobile k and the (mQk + q)-th chip of the pseudo-noise of arrival, delays, and medium term average path losses. scrambling code. In this approach, we also presume that the phases of the different multi-paths are known. Therefore, the mobile II. DOWNLINK DATA MODEL stations have to transmit the values of the phases every We presume a channel with discrete multi-paths, thus, the slot. 0 (k ;m) In WCDMA the users are distinguished by different channel impulse response hk (t) is FIR: spreading codes [1] which change over time due to the Q scrambling of the transmitted sequences. In [2] the beam- 0 0 (k ;m) (k ;m) (3) forming vectors at the base stations were computed slot- hk (t) = hk;q δ(t − τk;q); q=1 wise, whereas in this contribution we favour a symbol rate X evaluation of the beamforming vectors. The optimization where δ(t) denotes the Dirac delta function. Each tap 0 approach in [2] compensates the non-ideal properties of (k ;m) hk;q 2 C belongs to the path q over which the m-th the utilized spreading codes, which results in the optimal symbol of the signal k0 of the base station is transmitted solution with respect to the signal to interference and noise to the mobile station k (cf. Figure 1).The channel param- ratio. However, the new approach optimizes the actual eters estimated in the uplink are reciprocal in terms of the values of the decision variables of the rake demodulators. M DOA (steering vector ak;q 2 C , where M is the number of antenna elements), the medium term average path loss I. SIGNAL MODEL pk;q 2 R, and the path delay time τk;q. We assume that the path losses pk;q are numbered in a descending order, WCDMA was proposed by ETSI as a standard for FDD thus, pk;1 is the path loss of the strongest path to mobile k. services [3]. The downlink baseband signal for the mobile However, the reciprocity does not hold for the path phase k 2 f1; :::; Kg may be expressed as shift 'k;q . To eliminate this lack of knowledge we propose a feedback of the phase shift 'k;q from the mobile station 1 (m) (m) k to the base station. Because of the lower data rates in sk(t) = sk ck (t − mTk); (1) the uplink this feedback does not decrease the system ca- m=0 pacity. Therefore, the base station is able to compute the X 0 (k ;m) Qk q-th tap of hk (t): (m) (m) ck (t) = dk [q]prrc(t − qTc); (2) 0 (k ;m) H (m) j'k;q q=1 hk;q = pk;qak;qwk0 e ; (4) X (1) hk (t) s1(t) mobile s2(t) (2) hk (t) station k base station sK(t) inter-cell interference, (K) noise n (t) hk (t) k Figure 1: The channel of mobile station k. (m) M ∗ where wk0 2 C is the base station beamforming vec- where (•) and E[•] denote the complex conjugate and the tor for the mobile k0 which has to be estimated and (•)H expectation value, respectively. denotes conjugate transpose. Note that the channel impulse responses change from symbol to symbol since we deploy symbol-wise beam- (m) III. PREDEFINITION OF THE RAKE FINGER forming. The alteration of the beamforming vector wk0 depends on the changing correlation properties of the WEIGHTS scrambled spreading codes which will be outlined in the following sections. We further assume that the mobiles are Since we propose a symbol-wise beamforming for the equipped with a conventional maximum ratio combining downlink, the channel impulse responses also change rake receiver [4] (cf. Figure 2). First, the rake receiver es- from symbol to symbol. The rake demodulator exploits (k;m) timates the channel impulse response hk (t). After the the pilot symbols at the beginning of each slot to estimate taps of the channel estimation are used to perform a max- (k;m) the channel impulse response hk (t). imum ratio combining, the resulting signal is correlated Our approach is to keep the beamforming vectors con- with the spreading code of mobile k. Thus, the decision stant during the transmission of the pilot symbols. Thus, signal of the rake demodulator k for the symbol m can be only the correlation properties of the spreading codes written as: change due to scrambling, while the channel impulse re- K Q Nf (m) (m) sponse remains constant. The resulting channel estima- uk = Re CCFk;k0 (τk;f − τk;q)vk;f tion of the rake receiver is the mean over the estimations ( k0 q=1 f X=1 X X=1 using every pilot symbol. Therefore, the rake receiver esti- mates a constant channel with mean correlation functions. H (m) (m) j'k;q pk;qak;qwk0 sk0 e : (5) Note that the mean correlation functions of the scrambled ) spreading codes are ideal, i. e. the mean cross correla- K; Q; and Nf are the numbers of mobile stations, paths, tion is approximately zero and the mean auto correlation (m) and rake fingers, respectively. CCFk;k0 (τ) denotes the is equal to the spreading factor for the time instant zero cross correlation function of the spreading codes of the and approximately zero otherwise (cf. Figure 3 and 4). mobiles k and k0 for the symbol m. Note that the cross As a consequence, only the paths belonging to the rake correlation function is equal to the auto correlation func- fingers have to be taken into account for the channel esti- 0 tion, if k = k . The rake finger weights vk;f are derived mation process. from the channel estimation taps. Hence, pilot The base station beamforming vector w for the ∗ k (k;m) pilot symbols is computed to maximize the power of the vk;f = E hk;f channel impulse response at the rake receiver k, while the h (m)iH −j'k;f pilot pilot E w 0 a (6) H = pk;f ( k ) k;f e ; transmitted power (wk ) wk is the same for all mo- h i v1 τ1 − c(t) v2 Sample at t = T Received Detection signal r(t) T signal u τ2 ( ) dt Re − 0 • {•} R vQ τQ − Figure 2: Rake receiver for BPSK. Example of the Autocorrelation Function Example of the Crosscorrelation Function 20 20 15 15 10 10 ) ) τ τ ccf( acf( 5 5 0 0 −5 −5 −20 −15 −10 −5 0 5 10 15 20 −20 −15 −10 −5 0 5 10 15 20 τ τ Mean of the Autocorrelation Function Mean of the Crosscorrelation Function 20 20 15 15 10 10 ) ) τ τ ccf( acf( 5 5 0 0 −5 −5 −20 −15 −10 −5 0 5 10 15 20 −20 −15 −10 −5 0 5 10 15 20 τ τ Figure 3: Instantaneous and averaged autocorrelation Figure 4: Instantaneous and averaged crosscorrelation function of a scrambled spreading code of length SF = function of a scrambled spreading code of length SF = 16. 16. biles. That leads to following optimization problem: solution is the eigenvector belonging to the largest eigen- H value of AkAk . Nf The resulting predefined rake finger weight reads as: pilot H 2 H pilot max (wk ) ak;f pk;f ak;f wk = wpilot k f=1 pilot H −j'k;f X vk;f = pk;f (wk ) ak;f e : (7) pilot H H pilot max(wk ) AkAk wk ; wpilot k IV.
Load more

Recommended publications
  • Performance Comparisons of MIMO Techniques with Application to WCDMA Systems
    EURASIP Journal on Applied Signal Processing 2004:5, 649–661 c 2004 Hindawi Publishing Corporation Performance Comparisons of MIMO Techniques with Application to WCDMA Systems Chuxiang Li Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Xiaodong Wang Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Received 11 December 2002; Revised 1 August 2003 Multiple-input multiple-output (MIMO) communication techniques have received great attention and gained significant devel- opment in recent years. In this paper, we analyze and compare the performances of different MIMO techniques. In particular, we compare the performance of three MIMO methods, namely, BLAST, STBC, and linear precoding/decoding. We provide both an analytical performance analysis in terms of the average receiver SNR and simulation results in terms of the BER. Moreover, the applications of MIMO techniques in WCDMA systems are also considered in this study. Specifically, a subspace tracking algo- rithm and a quantized feedback scheme are introduced into the system to simplify implementation of the beamforming scheme. It is seen that the BLAST scheme can achieve the best performance in the high data rate transmission scenario; the beamforming scheme has better performance than the STBC strategies in the diversity transmission scenario; and the beamforming scheme can be effectively realized in WCDMA systems employing the subspace tracking and the quantized feedback approach. Keywords and phrases: BLAST, space-time block coding, linear precoding/decoding, subspace tracking, WCDMA. 1. INTRODUCTION ing power and/or rate over multiple transmit antennas, with partially or perfectly known channel state information [7].
    [Show full text]
  • IF-Sampling Digital Beamforming with Bit-Stream Processing by Jaehun
    IF-Sampling Digital Beamforming with Bit-Stream Processing by Jaehun Jeong A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Electrical Engineering) in the University of Michigan 2015 Doctoral Committee: Professor Michael P. Flynn, Chair Professor Jerome P. Lynch Associate Professor David D. Wentzloff Associate Professor Zhengya Zhang © Jaehun Jeong 2015 TABLE OF CONTENTS LIST OF FIGURES iv LIST OF TABLES vii LIST OF ABBREVIATIONS viii ABSTRACT x CHAPTER 1 Introduction 1 1.1 Beamforming and Its Applications 1 1.2 Narrowband and Wideband Beamforming 2 1.3 Beamforming in Receivers 3 1.4 Beamforming Receiver Architectures 6 1.4.1 Analog Beamforming 7 1.4.2 Digital Beamforming 8 1.5 Finite Complex Weight Resolution Effect on Phase Shifting 10 1.6 Thesis Overview 12 CHAPTER 2 IF-Sampling DBF with CTBPDSMs and BSP 14 2.1 DBF with Direct IF Sampling 16 2.2 Bit-Stream Processing DBF with ΔΣ Modulator Outputs 17 2.3 Mathematical Expressions of DBF with Band-Pass ADCs 20 ii 2.3.1 Beamforming with Single-Tone Inputs 21 2.3.2 Beamforming with Amplitude-Modulated Inputs 23 2.4 Prototype BSP Beamformers 25 2.4.1 MUX-based DDC and Phase Shifting 27 2.4.2 Summation 30 2.4.3 Decimation 30 2.5 Comparison between DSP and BSP 32 CHAPTER 3 Continuous-Time Band-Pass ΔΣ Modulator 35 3.1 Architecture 35 3.2 Circuit Implementation 39 3.2.1 Single Op-Amp Resonator 40 3.2.2 Quantizer 43 3.2.3 Current Steering DAC 44 CHAPTER 4 Measurements 46 4.1 Prototype I 46 4.2 Prototype II 49 CHAPTER 5 Future Work 58
    [Show full text]
  • Limitations, Performance and Instrumentation of Closed-Loop Feedback Based Distributed Adaptive Transmit Beamforming in Wsns
    Limitations, performance and instrumentation of closed-loop feedback based distributed adaptive transmit beamforming in WSNs Stephan Sigg, Rayan Merched El Masri, Julian Ristau and Michael Beigl Institute of operating systems and computer networks, TU Braunschweig Muhlenpfordtstrasse¨ 23, 38106 Braunschweig, Germany fsigg,[email protected], fj.ristau,[email protected] Abstract—We study closed-loop feedback based approaches to Also, by virtual MIMO techniques in wireless sensor net- distributed adaptive transmit beamforming in wireless sensor works [9], [10], single antenna nodes may establish a dis- networks. For a global random search scheme we discuss the tributed antenna array to generate a MIMO channel. Virtual impact of the transmission distance on the feasibility of the synchronisation approach. Additionally, a quasi novel method MIMO is energy efficient and adjusts to different frequencies for phase synchronisation of distributed adaptive transmit beam- [11], [12]. forming in wireless sensor networks is presented that improves In all these implementations, a dense population of nodes the synchronisation performance. Finally, we present measure- is assumed. A large scale sensor network, however, might be ments from an instrumentation using USRP software radios at sparsely populated by nodes as additional nodes increase the various transmit frequencies and with differing network sizes. installation cost. Alternative solutions are approaches in which a synchronisation among nodes is achieved over a communica- I. INTRODUCTION tion with the remote receiver. This means that communication A much discussed topic related to wireless sensor networks among nodes is not required for synchronisation so that also is the energy consumption of nodes as this impacts the lifetime sparsely populated networks can be supported.
    [Show full text]
  • Compact Multilayer Yagi-Uda Based Antenna for Iot/5G Sensors
    sensors Article Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors Amélia Ramos 1,2,*, Tiago Varum 2 ID and João N. Matos 1,2 ID 1 Universidade de Aveiro, Campus Universitário de Santiago, 3810-135 Aveiro, Portugal; [email protected] 2 Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-135 Aveiro, Portugal; [email protected] * Correspondence: [email protected]; Tel.: +351-234-377-900 Received: 27 July 2018; Accepted: 30 August 2018; Published: 2 September 2018 Abstract: To increase the capacity and performance of communication systems, the new generation of mobile communications (5G) will use frequency bands in the mmWave region, where new challenges arise. These challenges can be partially overcome by using higher gain antennas, Multiple-Input Multiple-Output (MIMO), or beamforming techniques. Yagi-Uda antennas combine high gain with low cost and reduced size, and might result in compact and efficient antennas to be used in Internet of Thins (IoT) sensors. The design of a compact multilayer Yagi for IoT sensors is presented, operating at 24 GHz, and a comparative analysis with a planar printed version is shown. The stacked prototype reveals an improvement of the antenna’s main properties, achieving 10.9 dBi, 2 dBi more than the planar structure. In addition, the multilayer antenna shows larger bandwidth than the planar; 6.9 GHz compared with 4.42 GHz. The analysis conducted acknowledges the huge potential of these stacked structures for IoT applications, as an alternative to planar implementations. Keywords: Yagi-Uda; multilayered antenna; millimeter-waves; IoT 1. Introduction People’s interconnection was improved by 4G, making the communication more efficient, fluent, and natural.
    [Show full text]
  • 802.11Ac MU-MIMO Bridging the MIMO Gap in Wi-Fi
    Title Qualcomm Atheros, Inc. 802.11ac MU-MIMO: Bridging the MIMO Gap in Wi-Fi January, 2015 Qualcomm Atheros, Inc. Not to be used, copied, reproduced, or modified in whole or in part, nor its contents revealed in any manner to others without the express written permission of Qualcomm Atheros Inc. Qualcomm, Snapdragon, and VIVE are trademarks of Qualcomm Incorporated, registered in the United States and other countries. All Qualcomm Incorporated trademarks are used with permission. Other product and brand names may be trademarks or registered trademarks of their respective owners. This technical data may be subject to U.S. and international export, re-export, or transfer (“export”) laws. Diversion contrary to U.S. and international law is strictly prohibited. Qualcomm Atheros, Inc. 1700 Technology Drive San Jose, CA 95110 U.S.A. ©2014-15 Qualcomm Atheros, Inc. All Rights Reserved. MAY CONTAIN U.S. AND INTERNATIONAL EXPORT CONTROLLED INFORMATION Page 2 Table of Contents 1 Executive Summary ................................................................................................................................ 4 2 Why MU-MIMO? ..................................................................................................................................... 5 3 11ac Advanced Features: Transmit Beamforming (TxBF) and MU-MIMO ............................................. 6 3.1 Standardized Closed Loop TxBF .................................................................................................... 6 3.2 MU-MIMO or MU-TxBF ..................................................................................................................
    [Show full text]
  • Directional Beamforming for Millimeter-Wave MIMO Systems
    Directional Beamforming for Millimeter-Wave MIMO Systems Vasanthan Raghavan, Sundar Subramanian, Juergen Cezanne and Ashwin Sampath Qualcomm Corporate R&D, Bridgewater, NJ 08807 E-mail: {vraghava, sundars, jcezanne, asampath}@qti.qualcomm.com Abstract—The focus of this paper is on beamforming in a that allows the deployment of a large number of antennas in millimeter-wave (mmW) multi-input multi-output (MIMO) set- a fixed array aperture. up that has gained increasing traction in meeting the high data- Despite the possibility of multi-input multi-output (MIMO) rate requirements of next-generation wireless systems. For a given MIMO channel matrix, the optimality of beamforming communications, mmW signaling differs significantly from with the dominant right-singular vector (RSV) at the transmit traditional MIMO architectures at cellular frequencies. The end and with the matched filter to the RSV at the receive most optimistic antenna configurations1 at cellular frequencies end has been well-understood. When the channel matrix can are on the order of 4 × 8 with a precoder rank (number of be accurately captured by a physical (geometric) scattering layers) of 1 to 4; see, e.g., [9]. Higher rank signaling requires model across multiple clusters/paths as is the case in mmW 2 MIMO systems, we provide a physical interpretation for this multiple radio-frequency (RF) chains which are easier to real- optimal structure: beam steering across the different paths with ize at lower frequencies than at the mmW regime. Thus, there appropriate power allocation and phase compensation. While has been a growing interest in understanding the capabilities of such an explicit physical interpretation has not been provided low-complexity approaches such as beamforming (that require hitherto, practical implementation of such a structure in a mmW only a single RF chain) in mmW systems [10]–[15].
    [Show full text]
  • MIMO Technology in Wifi Systems
    MIMO in WiFi Systems Rohit U. Nabar Smart Antenna Workshop Aug. 1, 2014 WiFi • Local area wireless technology that allows communication with the internet using 2.4 GHz or 5 GHz radio waves per IEEE 802.11 • Proliferation in the number of devices that use WiFi today: smartphones, tablets, digital cameras, video-game consoles, TVs, etc • Devices connect to the internet via wireless network access point (AP) Advantages • Allows convenient setup of local area networks without cabling – rapid network connection and expansion • Deployed in unlicensed spectrum – no regulatory approval required for individual deployment • Significant competition between vendors has driven costs lower • WiFi governed by a set of global standards (IEEE 802.11) – hardware compatible across geographical regions WiFi IC Shipment Growth 1.25x 15x Cumulative WiFi Devices in Use Data by Local Access The IEEE 802.11 Standards Family Standard Year Ratified Frequency Modulation Channel Max. Data Band Bandwidth Rate 802.11b 1999 2.4 GHz DSSS 22MHz 11 Mbps 802.11a 1999 5 GHz OFDM 20 MHz 54 Mbps 802.11g 2003 2.4 GHz OFDM 20 MHz 54 Mbps 802.11n 2009 2.4/5 GHz MIMO- 20,40 MHz 600 Mbps OFDM 802.11ac 2013 5 GHz MIMO- 20, 40, 80, 6.93 Gbps OFDM 160 MHz 802.11a/ac PHY Comparison 802.11a 802.11ac Modulation OFDM MIMO-OFDM Subcarrier spacing 312.5 KHz 312.5 KHz Symbol Duration 4 us (800 ns guard interval) 3.6 us (400 ns guard interval) FFT size 64 64(20 MHz)/512 (160 MHz) FEC BCC BCC or LDPC Coding rates 1/2, 2/3, 3/4 1/2, 2/3, 3/4, 5/6 QAM BPSK, QPSK, 16-,64-QAM BPSK, QPSK, 16-,64-,256- QAM Factors Driving the Data Rate Increase 6.93 Gbps QAM 802.11ac (1.3x) FEC rate (1.1x) MIMO (8x) 802.11a Bandwidth (8x) 54 Mbps 802.11 Medium Access Control (MAC) Contention MEDIUM BUSY DIFS PACKET Window • Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) • A wireless node that wants to transmit performs the following sequence 1.
    [Show full text]
  • Litepoint's Complete Guide to 5G OTA Testing
    ARTICLE LitePoint’s Complete Guide to 5G OTA Testing UPDATED: MAY 20, 2019 © 2019 LitePoint, A Teradyne Company. All rights reserved. Introduction To help engineers navigate the challenges of 5G OTA testing, LitePoint has put together this comprehensive guide to 5G OTA testing, featuring articles previously published at EDN, Microwaves & RF and RF Globalnet. Link-budget Calculations: Needed for 5G OTA Testing (page 3) In his article at EDN, LitePoint’s Jeorge Hurtarte explores how to calculate the total link budget for OTA test. Over-the-Air Testing for 5G mmWave Devices: DFF or CATR? (page 8) At Microwaves & RF Hurtarte and LitePoint’s Middle Wen explain the differences between DFF and CATR chambers and the tradeoffs in cost and path-loss performance between the two types of chambers. Understanding 5G Millimeter Wave Beamforming Test (page 14) Finally, in the article at RF Globalnet, Jeorge Hurtarte and LitePoint’s Vito Liao explain beamforming in the context of 5G NR specifications, and introduces beamforming characterization and beamforming verification test. Updated: May 20, 2019 LitePoint’s Complete Guide to 5G OTA Testing 2 Link-budget Calculations: Needed for 5G OTA Testing Previously published at EDN on January 22, 2019 5G millimeter wave (mmWave) devices operating above 24 GHz incorporate millimeter-sized patch antenna arrays or dipole antennas that become an integral part of the device module packaging. Testing these assemblies requires over-the-air tests inside a chamber. But, a mmWave test chamber can introduce significant path loss, more than from cables and connectors. Understanding how to calculate the total link budget for over-the-air testing is a critical step in 5G mmWave OTA test.
    [Show full text]
  • MIMO Fading Channel Capacity, Massive MIMO, Beamforming, Diversity Vs
    Lecture 15 - EE 359: Wireless Communications - Winter 2020 MIMO Fading Channel Capacity, Massive MIMO, Beamforming, Diversity vs. Multiplexing, RX Design Lecture Outline • MIMO Fading Channel Capacity • Massive MIMO • Beamforming • MIMO Diversity/Multiplexing Tradeoffs • MIMO RX Design 1. MIMO Fading Channel Capacity • In fading, capacity with both transmitter and receiver knowledge is the average of the capacity for the static channel, with power allocated either by an instantaneous or average power constraint. Under the instantaneous constraint power is optimally allocated over the spatial dimension only. Under the average constraint it is allocated over both space and time. • In fading, if the channel is unknown at transmitter, uniform power allocation is optimal, but this leads to an outage probability since the transmitter doesn’t know what rate R to transmit at: ρ H Pout = p H : B log2 det IMr + HH <R . Mt 2. Massive MIMO: • When the number of TX (or RX) antennas is large, the channel becomes “static” due to the law of large numbers. Specifically 1 H lim HH = IMr . Mt→∞ Mt • The MIMO channel capacity then becomes ρ H lim B log2 det IMr + HH = B log2 det [IMr + ρIMr ] = MrB log2(1 + ρ). Mt→∞ Mt • Defining M = min(Mt, Mr), this implies that as Mt grows large, the MIMO channel capacity in the absence of TX CSI approaches C = MB log2(1 + ρ) for ρ the SNR, and hence grows linearly in M. This is also true for finite Mt and Mr but at large SNR. 3. MIMO Systems: Beamforming • Beamforming sends the same symbol over each transmit antenna with a different scale factor.
    [Show full text]
  • Millimeter-Wave Evolution for 5G Cellular Networks
    1 Millimeter-wave Evolution for 5G Cellular Networks Kei SAKAGUCHI†a), Gia Khanh TRAN††, Hidekazu SHIMODAIRA††, Shinobu NANBA†††, Toshiaki SAKURAI††††, Koji TAKINAMI†††††, Isabelle SIAUD*, Emilio Calvanese STRINATI**, Antonio CAPONE***, Ingolf KARLS****, Reza AREFI****, and Thomas HAUSTEIN***** SUMMARY Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5 GHz band.
    [Show full text]
  • Beamforming Techniques for Wireless Communications in Low-Rank Channels: Analytical Models and Synthesis Algorithms
    UNIVERSITA` DEGLI STUDI DI TRIESTE DIPARTIMENTO DI ELETTROTECNICA, ELETTRONICA ED INFORMATICA XX Ciclo del Dottorato di Ricerca in Ingegneria dell’Informazione (Settore scientifico-disciplinare: ING-INF/03) TESI DI DOTTORATO Beamforming Techniques for Wireless Communications in Low-Rank Channels: Analytical Models and Synthesis Algorithms Dottorando Coordinatore MASSIMILIANO COMISSO Chiar.mo Prof. Alberto Bartoli (Università degli Studi di Trieste) Tutore Chiar.mo Prof. Fulvio Babich (Università degli Studi di Trieste) Relatore Chiar.mo Prof. Lucio Mania` (Università degli Studi di Trieste) Correlatore Chiar.mo Prof. Roberto Vescovo (Università degli Studi di Trieste) Summary The objective of this thesis is discussing the application of multiple antenna tech- nology in some selected areas of wireless networks and fourth-generation telecom- munication systems. The original contributions of this study involve, mainly, two research fields in the context of the emerging solutions for high-speed digital com- munications: the mathematical modeling of distributed wireless networks adopt- ing advanced antenna techniques and the development of iterative algorithms for antenna array pattern synthesis. The material presented in this dissertation is the result of three-year studies performed within the Telecommunication Group of the Department of Electronic Engineering at the University of Trieste during the course of Doctorate in Information Engineering. In recent years, an enormous increase in traffic has been experienced by wire- less communication
    [Show full text]
  • Fast Beamforming of Electronically Steerable Parasitic Array Radiator
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 52, NO. 7, JULY 2004 1819 Fast Beamforming of Electronically Steerable Parasitic Array Radiator Antennas: Theory and Experiment Chen Sun, Student Member, IEEE, Akifumi Hirata, Member, IEEE, Takashi Ohira, Fellow, IEEE, and Nemai C. Karmakar, Senior Member, IEEE Abstract—A low-power consumption, small-size smart antenna, named electronically steerable parasitic array radiator (ESPAR), has been designed. Beamforming is achieved by tuning the load reactances at parasitic elements surrounding the active central element. A fast beamforming algorithm based on simultaneous perturbation stochastic approximation with a maximum cross correlation coefficient criterion is proposed. The simulation and experimental results validate the algorithm. In an environment where the signal-to-interference-ratio is 0 dB, the algorithm converges within 50 iterations and achieves an output signal-to- interference-plus-noise-ratio of 10 dB. With the fast beamforming ability and its low-power consumption attribute, the ESPAR antenna makes the mass deployment of smart antenna technologies practical. Index Terms—Ad hoc network, aerial beamforming (ABF), dig- ital beamforming (DBF), electronically steerable parasitic array Fig. 1. Functional block diagram of DBF antenna arrays using phased array technology. radiator (ESPAR), simultaneous perturbation stochastic approx- imation (SPSA), smart antennas, spatial division multiple access (SDMA). with the direction finding ability of smart antennas [1], [2]. It also has the applications in ad hoc networks or wireless local-area networks (WLANs), for example employed at mo- I. INTRODUCTION bile terminals (notebooks, PDAs, etc.) in a wireless network. HE EXPLOSIVE growth of wireless communications The direction finding ability avails the design of the packet T industry is creating a huge market opportunity.
    [Show full text]