Lecture 7 MIMO Communica2ons
|
|
- Johnathan Barrett
- 5 years ago
- Views:
Transcription
1 Wireless Communications Lecture 7 MIMO Communica2ons Prof. Chun-Hung Liu Dept. of Electrical and Computer Engineering National Chiao Tung University Fall
2 Outline MIMO Communications (Chapter 10 in Goldsmith s Book) Narrowband MIMO Model Parallel Decomposition of the MIMO Channel MIMO Channel Capacity MIMO Diversity Gain: Beamforming Diversity-Multiplexing Trade-off Space-Time Modulation and Coding 2
3 Circular Complex Gaussian Vectors A complex random vector is of the form where and are real random vectors. Complex Gaussian random vectors are ones in which [x R, x I ] t is a real Gaussian random vector. The distribution is completely specified by the mean and covariance matrix of the real vector [x R, x I ] t. Define x = x R + jx I x R x I where A is the transpose of the matrix A with each element replaced by its complex conjugate, and is just the transpose of A. Note that in general the covariance matrix K of the complex random vector x by itself is not enough to specify the full second-order statistics of x. Indeed, since K is Hermitian, i.e., K = K, the diagonal elements are real and the elements in the lower and upper triangles are complex conjugates of each other. A t 3
4 Circular Complex Gaussian Vectors In wireless communication, we are almost exclusively interested in complex random vectors that have the circular symmetry property: For a circular symmetric complex random vector x, for any ; hence the mean µ =0. Moreover for any ; hence the pseudo-covariance matrix J is also zero. Thus, the covariance matrix K fully specifies the first and second order statistics of a circular symmetric random vector. 4
5 Circular Complex Gaussian Vectors And if the complex random vector is also Gaussian, K in fact specifies its entire statistics. A circular symmetric Gaussian random vector with covariance matrix K is denoted as. Some special cases: A complex Gaussian random variable w = w R + jw I with i.i.d. zero-mean Gaussian real and imaginary components is circular symmetric. In fact, a circular symmetric Gaussian random variable must have i.i.d. zero-mean real and imaginary components. A collection of n i.i.d. CN(0, 1) random variables forms a standard circular symmetric Gaussian random vector w and is denoted by CN(0, I). The density function of w can be explicitly written as Uw has the same distribution as w for any complex orthogonal matrix U (such a matrix is called a unitary matrix and characterized by the property. UU = I 5
6 Narrowband MIMO Model Here we consider a narrowband MIMO channel. A narrowband point-topoint communication system of transmit and receive antennas is shown in the following figure. 6
7 Or simply as Narrowband MIMO Model We assume a channel bandwidth of B and complex Gaussian noise with zero mean and covariance matrix, where typically. For simplicity, given a transmit power constraint P we will assume an equivalent model with a noise power of unity and transmit power, where ρ can be interpreted as the average SNR per receive antenna under unity channel gain. This power constraint implies that the input symbols satisfy (10.1) where is the trace of the input covariance matrix 7
8 Parallel Decomposition of the MIMO Channel When both the transmitter and receiver have multiple antennas, there is another mechanism for performance gain called (spatial) multiplexing gain. The multiplexing gain of an MIMO system results from the fact that a MIMO channel can be decomposed into a number R of parallel independent channels. By multiplexing independent data onto these independent channels, we get an R-fold increase in data rate in comparison to a system with just one antenna at the transmitter and receiver. Consider a MIMO channel with channel gain matrix known to both the transmitter and the receiver. Let denote the rank of H. From matrix theory, for any matrix H we can obtain its singular value decomposition (SVD) as (10.2) where Σ is an diagonal matrix of singular values of H. 8
9 Parallel Decomposition of the MIMO Channel Since cannot exceed the number of columns or rows of H, If H is full rank, which is sometimes referred to as a rich scattering environment, then. The parallel decomposition of the channel is obtained by defining a transformation on the channel input and output x and y through transmit precoding and receiver shaping, as shown in the following figure: The transmit precoding and receiver shaping transform the MIMO channel into RH parallel single-input single-output (SISO) channels with input and output, since from the SVD, we have that 9
10 Parallel Decomposition of the MIMO Channel 10
11 Parallel Decomposition of the MIMO Channel This parallel decomposition is shown in the following figure: 11
12 MIMO Channel Capacity The capacity of a MIMO channel is an extension of the mutual information formula for a SISO channel given in Lecture 3 to a matrix channel. Specifically, the capacity is given in terms of the mutual information between the channel input vector x and output vector y as (10.5) The definition of entropy yields that H(Y X) = H(N), the entropy in the noise. Since this noise n has fixed entropy independent of the channel input, maximizing mutual information is equivalent to maximizing the entropy in y. The mutual information of y depends on its covariance matrix, which for the narrowband MIMO model is given by (10.6) 12
13 MIMO Channel Capacity where R x is the covariance of the MIMO channel input. The mutual information can be shown as (10.7) The MIMO capacity is achieved by maximizing the mutual information over all input covariance matrices satisfying the power constraint: (10.8) where denotes the determinant of the matrix A. Now let us consider the case of Channel Known at Transmitter 13
14 MIMO Channel Capacity Substituting the matrix SVD of H into C and using properties of unitary matrices we get the MIMO capacity with CSIT and CSIR as (10.9) Since = P/ n 2, the capacity (10.9) can also be expressed in terms of the power allocation to the ith parallel channel as P i (10.10) where i = P i / n 2 and i = i 2P/ n 2 is the SNR associated with the ith channel at full power. Solving the optimization leads to a water-filling power allocation for the MIMO channel: (10.11) 14
15 The resulting capacity is then MIMO Channel Capacity (10.12) Now consider the case of Channel Unknown at Transmitter (Uniform Power Allocation) Suppose now that the receiver knows the channel but the transmitter does not. Without channel information, the transmitter cannot optimize its power allocation or input covariance structure across antennas. If the distribution of H follows the ZMSW channel gain model, there is no bias in terms of the mean or covariance of H. Thus, it seems intuitive that the best strategy should be to allocate equal power to each transmit antenna, resulting in an input covariance matrix equal to the scaled identity matrix: 15
16 MIMO Channel Capacity It is shown that under these assumptions this input covariance matrix indeed maximizes the mutual information of the channel. For an Mt transmit, Mr-receive antenna system, this yields mutual information given by (10.13) Using the SVD of H, we can express this as where and is the number of nonzero singular values of H. The mutual information of the MIMO channel (10.13) depends on the specific realization of the matrix H, in particular its singular values { i }. 16
17 MIMO Channel Capacity In capacity with outage the transmitter fixes a transmission rate C, and the outage probability associated with C is the probability that the transmitted data will not be received correctly or, equivalently, the probability that the channel H has mutual information less than C. This probability is given by (10.14) Note that for fixed M r, under the ZMSW model the law of large numbers implies that (10.15) Substituting this into (10.13) yields that the mutual information in the asymptotic limit of large becomes a constant equal to M t 17
18 MIMO Channel Capacity We can have two important observations from the results in (10.14) and (10.15) As SNR grows large, capacity also grows linearly with for any and. At very low SNRs transmit antennas are not beneficial: Capacity only scales with the number of receive antennas independent of the number of transmit antennas. M t M r M =min{m t,m r } Fading Channels Channel Known at Transmitter: Water-Filling (10.16) 18
19 MIMO Channel Capacity A less restrictive constraint is a long-term power constraint, where we can use different powers for different channel realizations subject to the average power constraint over all channel realizations. The ergodic capacity in this case is (10.17) Channel Unknown at Transmitter: Ergodic Capacity and Capacity with Outage Consider now a time-varying channel with random matrix H known at the receiver but not the transmitter. The transmitter assumes a ZMSW distribution for H. The two relevant capacity definitions in this case are ergodic capacity and capacity with outage. Ergodic capacity defines the maximum rate, averaged over all channel realizations, that can be transmitted over the channel for a transmission strategy based only on the distribution of H. 19
20 MIMO Channel Capacity This leads to the transmitter optimization problem - i.e., finding the optimum input covariance matrix to maximize ergodic capacity subject to the transmit power constraint. Mathematically, the problem is to characterize the optimum R x to maximize (10.18) where the expectation is with respect to the distribution on the channel matrix H, which for the ZMSW model is i.i.d. zero-mean circularly symmetric unit variance. As in the case of scalar channels, the optimum input covariance matrix that maximizes ergodic capacity for the ZMSW model is the scaled identity matrix. Thus the ergodic capacity is given by: M t I Mt (10.19) 20
21 MIMO Channel Capacity The ergodic capacity of a 4x4 MIMO system with i.i.d. complex Gaussian channel gains is shown in Figure
22 MIMO Channel Capacity Capacity with outage is defined similar to the definition for static channels described in previous, although now capacity with outage applies to a slowly-varying channel where the channel matrix H is constant over a relatively long transmission time, then changes to a new value. As in the static channel case, the channel realization and corresponding channel capacity is not known at the transmitter, yet the transmitter must still fix a transmission rate to send data over the channel. For any choice of this rate C, there will be an outage probability associated with C, which defines the probability that the transmitted data will not be received correctly. The outage capacity can sometimes be improved by not allocating power to one or more of the transmit antennas, especially when the outage probability is high. This is because outage capacity depends on the tail of the probability distribution. With fewer antennas, less averaging takes place and the spread of the tail increases. 22
23 MIMO Channel Capacity The capacity with outage of a 4 4 MIMO system with i.i.d. complex Gaussian channel gains is shown in Figure
24 MIMO Channel Capacity 24
25 MIMO Diversity Gain: Beamforming The multiple antennas at the transmitter and receiver can be used to obtain diversity gain instead of capacity gain. In this setting, the same symbol, weighted by a complex scale factor, is sent over each transmit antenna, so that the input covariance matrix has unit rank. This scheme is also referred to as MIMO beamforming. A beamforming strategy corresponds to the precoding and receiver matrices described in previous being just column vectors: V = v and U = u, as shown in Figure In the figure, the transmit symbol x is sent over the ith antenna with weight v i. On the receive side, the signal received on the ith antenna is weighted by u i. Both transmit and receive weight vectors are normalized so that kuk = kvk =1 The resulting received signal is given by where if n =(n 1,,n Mr ) has i.i.d. elements. (10.20) 25
26 MIMO Diversity Gain: Beamforming Beamforming provides diversity gain by coherent combining of the multiple signal paths. Channel knowledge at the receiver is typically assumed since this is required for coherent combining. 26
27 MIMO Diversity Gain: Beamforming The diversity gain then depends on whether or not the channel is known at the transmitter. When the channel matrix H is known, the received SNR is optimized by choosing u and v as the principal left and right singular vectors of the channel matrix H. The corresponding received SNR can be shown to equal = max, where max is the largest eigenvalue of the Wishart matrix W = H H H. The resulting capacity is C = B log 2 (1 + max ), corresponding to the capacity of a SISO channel with channel power gain max. When the channel is not known at the transmitter, the transmit antenna weights are all equal, so the received SNR equals = khu k, where u is chosen to maximize γ. Clearly the lack of transmitter CSI will result in a lower SNR and capacity than with optimal transmit weighting. 27
28 Diversity-Multiplexing Tradeoffs So far, we already knew that there are two mechanisms for utilizing multiple antennas to improve wireless system performance. One option is to obtain capacity gain by decomposing the MIMO channel into parallel channels and multiplexing different data streams onto these channels. This capacity gain is also referred to as a multiplexing gain. It is not necessary to use the antennas purely for multiplexing or diversity. Some of the space-time dimensions can be used for diversity gain, and the remaining dimensions used for multiplexing gain. This gives rise to a fundamental design question in MIMO systems: should the antennas be used for diversity gain, multiplexing gain, or both? The diversity/multiplexing tradeoff or, more generally, the tradeoff between data rate, probability of error, and complexity for MIMO systems has been extensively studied in the literature, from both a theoretical perspective and in terms of practical space-time code designs. This work has primarily focused on block fading channels with receiver CSI only since when both transmitter and receiver know the channel the tradeoff is relatively straightforward. 28
29 Diversity-Multiplexing Tradeoffs Antenna subsets can first be grouped for diversity gain and then the multiplexing gain corresponds to the new channel with reduced dimension due to the grouping. For finite blocklengths it is not possible to achieve full diversity and full multiplexing gain simultaneously, in which case there is a tradeoff between these gains. A transmission scheme is said to achieve multiplexing gain r and diversity gain d if the data rate (bps) per unit Hertz R(SNR) and probability of error P e (SNR) as functions of SNR satisfy (10.21) (10.22) 29
30 Diversity-Multiplexing Tradeoffs For each r the optimal diversity gain d opt (r) is the maximum the diversity gain that can be achieved by any scheme. It is shown that if the fading block length exceeds the total number of antennas at the transmitter and receiver, then The function (10.23) is plotted in Fig (10.23) 30
31 Space-Time Modulation and Coding Since a MIMO channel has input-output relationship y = Hx + n, the symbol transmitted over the channel each symbol time is a vector rather than a scalar, as in traditional modulation for the SISO channel. Moreover, when the signal design extends over both space (via the multiple antennas) and time (via multiple symbol times), it is typically referred to as a space-time code. Most space-time codes are designed for quasi-static channels where the channel is constant over a block of T symbol times, and the channel is assumed unknown at the transmitter. Let X =[x 1,...,x T ] denote the M t T channel input matrix with ith column x i equal to the vector channel input over the ith transmission time. Let Y =[y 1,...,y T ] denote the M t T channel output matrix with ith column y i equal to the vector channel output over the ith transmission time. Let N =[n 1,...,n T ] denote the M r T noise matrix with ith column n i equal to the receiver noise vector on the ith transmission time. 31
32 ML Detection and Pairwise Error Probability With this matrix representation the input-output relationship over all T blocks becomes (10.24) Assume a space-time code where the receiver has knowledge of the channel matrix H. Under ML detection and given received matrix Y, the ML transmit matrix satisfies ˆX (10.25) where kak F denotes the Frobenius norm of the matrix A and the minimization is taken over all possible space time input matrices. The pairwise error probability for mistaking a transmit matrix X for another matrix ˆX, denoted as p( ˆX! X), depends only on the distance between the two matrices after transmission through the channel and the noise power, i.e. X T 32
33 ML Detection and Pairwise Error Probability (10.26) Let D x = X ˆX denote the difference matrix between X and ˆX. Applying the Chernoff bound to (10.26) yields (10.27) Let h i denote the ith row of H, i =1,...,M r. Then (10.28) Let H =vec(h T ) T where vec(a) is defined as the vector that results from stacking the columns of matrix A on top of each other to form a vector. 33
34 ML Detection and Pairwise Error Probability So H T is a vector of length M t M r. Also define D X = I Mr D x, where denotes the Kronecker product. With these definitions, khd x k 2 F = khd x k 2 = HD x D H x H H (10.29) Substituting (10.29) into (10.27) and taking the expectation relative to all possible channel realizations yields apple p(x! ˆX) apple det I Mt M r Dx H H H HD x (10.30) n Suppose that the channel matrix H is random and spatially white, so that its entries are i.i.d. zero-mean unit variance complex Gaussian random variables. Then taking the expectation yields where = 1 P DH x D x p(x! ˆX) apple det apple I Mt + P 4 2 n Mr (10.31) 34
35 ML Detection and Pairwise Error Probability p(x! ˆX) apple NY k= k ( )/4 Mr (10.32) = P 2 n 4 N Mr (10.33) Rank and Determinant Criterion: The pairwise error probability in d (10.33) indicates that the probability of error decreases as for d = N M r Thus, N M r is the diversity gain of the space-time code. The maximum diversity gain possible through coherent combining of M t transmit and receive antennas is. M r M t 35 M r
36 ML Detection and Pairwise Error Probability To obtain this maximum diversity gain, the space-time code must be designed such that the M t M t difference matrix Δ between any two code words has full rank equal to M t. This design criterion is referred to as the rank criterion. The coding gain associated with the pairwise error probability in (10.33) depends on the first term A high coding gain is achieved by maximizing the minimum of the determinant of Δ over all input matrix pairs X and ˆX. This criterion is referred to as the determinant criterion. 36
37 Spatial Multiplexing and BLAST Architectures In order to get full diversity order an encoded bit stream must be transmitted over all M t transmit antennas. This can be done through a serial encoding, illustrated in Figure
38 Spatial Multiplexing and BLAST Architectures A simpler method to achieve spatial multiplexing, pioneered at Bell Laboratories as one of the Bell Labs Layered Space Time (BLAST) architectures for MIMO channels, is parallel encoding, illustrated in Figure
39 Spatial Multiplexing and BLAST Architectures 39
40 Spatial Multiplexing and BLAST Architectures 40
ELEC E7210: Communication Theory. Lecture 10: MIMO systems
ELEC E7210: Communication Theory Lecture 10: MIMO systems Matrix Definitions, Operations, and Properties (1) NxM matrix a rectangular array of elements a A. an 11 1....... a a 1M. NM B D C E ermitian transpose
More informationLecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH
: Antenna Diversity and Theoretical Foundations of Wireless Communications Wednesday, May 4, 206 9:00-2:00, Conference Room SIP Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationSingle-User MIMO systems: Introduction, capacity results, and MIMO beamforming
Single-User MIMO systems: Introduction, capacity results, and MIMO beamforming Master Universitario en Ingeniería de Telecomunicación I. Santamaría Universidad de Cantabria Contents Introduction Multiplexing,
More information12.4 Known Channel (Water-Filling Solution)
ECEn 665: Antennas and Propagation for Wireless Communications 54 2.4 Known Channel (Water-Filling Solution) The channel scenarios we have looed at above represent special cases for which the capacity
More informationMultiple Antennas in Wireless Communications
Multiple Antennas in Wireless Communications Luca Sanguinetti Department of Information Engineering Pisa University luca.sanguinetti@iet.unipi.it April, 2009 Luca Sanguinetti (IET) MIMO April, 2009 1 /
More informationELEC546 MIMO Channel Capacity
ELEC546 MIMO Channel Capacity Vincent Lau Simplified Version.0 //2004 MIMO System Model Transmitter with t antennas & receiver with r antennas. X Transmitted Symbol, received symbol Channel Matrix (Flat
More informationDiversity-Multiplexing Tradeoff in MIMO Channels with Partial CSIT. ECE 559 Presentation Hoa Pham Dec 3, 2007
Diversity-Multiplexing Tradeoff in MIMO Channels with Partial CSIT ECE 559 Presentation Hoa Pham Dec 3, 2007 Introduction MIMO systems provide two types of gains Diversity Gain: each path from a transmitter
More informationSpace-Time Coding for Multi-Antenna Systems
Space-Time Coding for Multi-Antenna Systems ECE 559VV Class Project Sreekanth Annapureddy vannapu2@uiuc.edu Dec 3rd 2007 MIMO: Diversity vs Multiplexing Multiplexing Diversity Pictures taken from lectures
More informationLecture 8: MIMO Architectures (II) Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
MIMO : MIMO Theoretical Foundations of Wireless Communications 1 Wednesday, May 25, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 20 Overview MIMO
More informationExploiting Partial Channel Knowledge at the Transmitter in MISO and MIMO Wireless
Exploiting Partial Channel Knowledge at the Transmitter in MISO and MIMO Wireless SPAWC 2003 Rome, Italy June 18, 2003 E. Yoon, M. Vu and Arogyaswami Paulraj Stanford University Page 1 Outline Introduction
More informationThe Optimality of Beamforming: A Unified View
The Optimality of Beamforming: A Unified View Sudhir Srinivasa and Syed Ali Jafar Electrical Engineering and Computer Science University of California Irvine, Irvine, CA 92697-2625 Email: sudhirs@uciedu,
More informationCHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS
CHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS June Chul Roh and Bhaskar D Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 9293 47,
More informationErgodic and Outage Capacity of Narrowband MIMO Gaussian Channels
Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels Yang Wen Liang Department of Electrical and Computer Engineering The University of British Columbia April 19th, 005 Outline of Presentation
More information2318 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 6, JUNE Mai Vu, Student Member, IEEE, and Arogyaswami Paulraj, Fellow, IEEE
2318 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 6, JUNE 2006 Optimal Linear Precoders for MIMO Wireless Correlated Channels With Nonzero Mean in Space Time Coded Systems Mai Vu, Student Member,
More informationLimited Feedback in Wireless Communication Systems
Limited Feedback in Wireless Communication Systems - Summary of An Overview of Limited Feedback in Wireless Communication Systems Gwanmo Ku May 14, 17, and 21, 2013 Outline Transmitter Ant. 1 Channel N
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Wednesday, June 1, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationAdvanced Topics in Digital Communications Spezielle Methoden der digitalen Datenübertragung
Advanced Topics in Digital Communications Spezielle Methoden der digitalen Datenübertragung Dr.-Ing. Carsten Bockelmann Institute for Telecommunications and High-Frequency Techniques Department of Communications
More informationTransmit Directions and Optimality of Beamforming in MIMO-MAC with Partial CSI at the Transmitters 1
2005 Conference on Information Sciences and Systems, The Johns Hopkins University, March 6 8, 2005 Transmit Directions and Optimality of Beamforming in MIMO-MAC with Partial CSI at the Transmitters Alkan
More informationChapter 4: Continuous channel and its capacity
meghdadi@ensil.unilim.fr Reference : Elements of Information Theory by Cover and Thomas Continuous random variable Gaussian multivariate random variable AWGN Band limited channel Parallel channels Flat
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Friday, May 25, 2018 09:00-11:30, Kansliet 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless
More informationEE 5407 Part II: Spatial Based Wireless Communications
EE 5407 Part II: Spatial Based Wireless Communications Instructor: Prof. Rui Zhang E-mail: rzhang@i2r.a-star.edu.sg Website: http://www.ece.nus.edu.sg/stfpage/elezhang/ Lecture IV: MIMO Systems March 21,
More informationCapacity of multiple-input multiple-output (MIMO) systems in wireless communications
15/11/02 Capacity of multiple-input multiple-output (MIMO) systems in wireless communications Bengt Holter Department of Telecommunications Norwegian University of Science and Technology 1 Outline 15/11/02
More informationVECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK
VECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK June Chul Roh and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La
More informationSimultaneous SDR Optimality via a Joint Matrix Decomp.
Simultaneous SDR Optimality via a Joint Matrix Decomposition Joint work with: Yuval Kochman, MIT Uri Erez, Tel Aviv Uni. May 26, 2011 Model: Source Multicasting over MIMO Channels z 1 H 1 y 1 Rx1 ŝ 1 s
More informationParallel Additive Gaussian Channels
Parallel Additive Gaussian Channels Let us assume that we have N parallel one-dimensional channels disturbed by noise sources with variances σ 2,,σ 2 N. N 0,σ 2 x x N N 0,σ 2 N y y N Energy Constraint:
More informationMultiuser Capacity in Block Fading Channel
Multiuser Capacity in Block Fading Channel April 2003 1 Introduction and Model We use a block-fading model, with coherence interval T where M independent users simultaneously transmit to a single receiver
More informationHomework 5 Solutions. Problem 1
Homework 5 Solutions Problem 1 (a Closed form Chernoff upper-bound for the uncoded 4-QAM average symbol error rate over Rayleigh flat fading MISO channel with = 4, assuming transmit-mrc The vector channel
More informationOptimal Transmit Strategies in MIMO Ricean Channels with MMSE Receiver
Optimal Transmit Strategies in MIMO Ricean Channels with MMSE Receiver E. A. Jorswieck 1, A. Sezgin 1, H. Boche 1 and E. Costa 2 1 Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut 2
More informationOptimal Data and Training Symbol Ratio for Communication over Uncertain Channels
Optimal Data and Training Symbol Ratio for Communication over Uncertain Channels Ather Gattami Ericsson Research Stockholm, Sweden Email: athergattami@ericssoncom arxiv:50502997v [csit] 2 May 205 Abstract
More informationVector Channel Capacity with Quantized Feedback
Vector Channel Capacity with Quantized Feedback Sudhir Srinivasa and Syed Ali Jafar Electrical Engineering and Computer Science University of California Irvine, Irvine, CA 9697-65 Email: syed@ece.uci.edu,
More informationEE 5407 Part II: Spatial Based Wireless Communications
EE 5407 Part II: Spatial Based Wireless Communications Instructor: Prof. Rui Zhang E-mail: rzhang@i2r.a-star.edu.sg Website: http://www.ece.nus.edu.sg/stfpage/elezhang/ Lecture II: Receive Beamforming
More informationBlind MIMO communication based on Subspace Estimation
Blind MIMO communication based on Subspace Estimation T. Dahl, S. Silva, N. Christophersen, D. Gesbert T. Dahl, S. Silva, and N. Christophersen are at the Department of Informatics, University of Oslo,
More informationMultiple Antennas for MIMO Communications - Basic Theory
Multiple Antennas for MIMO Communications - Basic Theory 1 Introduction The multiple-input multiple-output (MIMO) technology (Fig. 1) is a breakthrough in wireless communication system design. It uses
More informationMaximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary. Spatial Correlation
Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary Spatial Correlation Ahmed K Sadek, Weifeng Su, and K J Ray Liu Department of Electrical and Computer Engineering, and Institute for Systems
More informationBlind Channel Identification in (2 1) Alamouti Coded Systems Based on Maximizing the Eigenvalue Spread of Cumulant Matrices
Blind Channel Identification in (2 1) Alamouti Coded Systems Based on Maximizing the Eigenvalue Spread of Cumulant Matrices Héctor J. Pérez-Iglesias 1, Daniel Iglesia 1, Adriana Dapena 1, and Vicente Zarzoso
More informationAppendix B Information theory from first principles
Appendix B Information theory from first principles This appendix discusses the information theory behind the capacity expressions used in the book. Section 8.3.4 is the only part of the book that supposes
More informationWITH PERFECT channel information at the receiver,
IEEE JOURNA ON SEECTED AREAS IN COMMUNICATIONS, VO. 25, NO. 7, SEPTEMBER 2007 1269 On the Capacity of MIMO Wireless Channels with Dynamic CSIT Mai Vu, Member, IEEE, and Arogyaswami Paulraj, Fellow, IEEE
More informationApplications of Lattices in Telecommunications
Applications of Lattices in Telecommunications Dept of Electrical and Computer Systems Engineering Monash University amin.sakzad@monash.edu Oct. 2013 1 Sphere Decoder Algorithm Rotated Signal Constellations
More informationTitle. Author(s)Tsai, Shang-Ho. Issue Date Doc URL. Type. Note. File Information. Equal Gain Beamforming in Rayleigh Fading Channels
Title Equal Gain Beamforming in Rayleigh Fading Channels Author(s)Tsai, Shang-Ho Proceedings : APSIPA ASC 29 : Asia-Pacific Signal Citationand Conference: 688-691 Issue Date 29-1-4 Doc URL http://hdl.handle.net/2115/39789
More informationDiversity Multiplexing Tradeoff in Multiple Antenna Multiple Access Channels with Partial CSIT
1 Diversity Multiplexing Tradeoff in Multiple Antenna Multiple Access Channels with artial CSIT Kaushi Josiam, Dinesh Rajan and Mandyam Srinath, Department of Electrical Engineering, Southern Methodist
More informationErgodic and Outage Capacity of Narrowband MIMO Gaussian Channels
Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels Yang Wen Liang Department of Electrical and Computer Engineering The University of British Columbia, Vancouver, British Columbia Email:
More informationPrecoded Integer-Forcing Universally Achieves the MIMO Capacity to Within a Constant Gap
Precoded Integer-Forcing Universally Achieves the MIMO Capacity to Within a Constant Gap Or Ordentlich Dept EE-Systems, TAU Tel Aviv, Israel Email: ordent@engtauacil Uri Erez Dept EE-Systems, TAU Tel Aviv,
More informationSpatial and Temporal Power Allocation for MISO Systems with Delayed Feedback
Spatial and Temporal ower Allocation for MISO Systems with Delayed Feedback Venkata Sreekanta Annapureddy and Srikrishna Bhashyam Department of Electrical Engineering Indian Institute of Technology Madras
More informationTight Lower Bounds on the Ergodic Capacity of Rayleigh Fading MIMO Channels
Tight Lower Bounds on the Ergodic Capacity of Rayleigh Fading MIMO Channels Özgür Oyman ), Rohit U. Nabar ), Helmut Bölcskei 2), and Arogyaswami J. Paulraj ) ) Information Systems Laboratory, Stanford
More informationSecure Degrees of Freedom of the MIMO Multiple Access Wiretap Channel
Secure Degrees of Freedom of the MIMO Multiple Access Wiretap Channel Pritam Mukherjee Sennur Ulukus Department of Electrical and Computer Engineering University of Maryland, College Park, MD 074 pritamm@umd.edu
More informationMulti-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems
Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User
More informationDirty Paper Coding vs. TDMA for MIMO Broadcast Channels
TO APPEAR IEEE INTERNATIONAL CONFERENCE ON COUNICATIONS, JUNE 004 1 Dirty Paper Coding vs. TDA for IO Broadcast Channels Nihar Jindal & Andrea Goldsmith Dept. of Electrical Engineering, Stanford University
More informationCommunication Over MIMO Broadcast Channels Using Lattice-Basis Reduction 1
Communication Over MIMO Broadcast Channels Using Lattice-Basis Reduction 1 Mahmoud Taherzadeh, Amin Mobasher, and Amir K. Khandani Coding & Signal Transmission Laboratory Department of Electrical & Computer
More informationAchieving the Full MIMO Diversity-Multiplexing Frontier with Rotation-Based Space-Time Codes
Achieving the Full MIMO Diversity-Multiplexing Frontier with Rotation-Based Space-Time Codes Huan Yao Lincoln Laboratory Massachusetts Institute of Technology Lexington, MA 02420 yaohuan@ll.mit.edu Gregory
More informationOptimum Transmission Scheme for a MISO Wireless System with Partial Channel Knowledge and Infinite K factor
Optimum Transmission Scheme for a MISO Wireless System with Partial Channel Knowledge and Infinite K factor Mai Vu, Arogyaswami Paulraj Information Systems Laboratory, Department of Electrical Engineering
More informationMULTI-INPUT multi-output (MIMO) channels, usually
3086 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 8, AUGUST 2009 Worst-Case Robust MIMO Transmission With Imperfect Channel Knowledge Jiaheng Wang, Student Member, IEEE, and Daniel P. Palomar,
More informationINVERSE EIGENVALUE STATISTICS FOR RAYLEIGH AND RICIAN MIMO CHANNELS
INVERSE EIGENVALUE STATISTICS FOR RAYLEIGH AND RICIAN MIMO CHANNELS E. Jorswieck, G. Wunder, V. Jungnickel, T. Haustein Abstract Recently reclaimed importance of the empirical distribution function of
More informationLecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1
Fading : Theoretical Foundations of Wireless Communications 1 Thursday, May 3, 2018 9:30-12:00, Conference Room SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 23 Overview
More informationRandom Matrix Theory Lecture 1 Introduction, Ensembles and Basic Laws. Symeon Chatzinotas February 11, 2013 Luxembourg
Random Matrix Theory Lecture 1 Introduction, Ensembles and Basic Laws Symeon Chatzinotas February 11, 2013 Luxembourg Outline 1. Random Matrix Theory 1. Definition 2. Applications 3. Asymptotics 2. Ensembles
More informationELEC546 Review of Information Theory
ELEC546 Review of Information Theory Vincent Lau 1/1/004 1 Review of Information Theory Entropy: Measure of uncertainty of a random variable X. The entropy of X, H(X), is given by: If X is a discrete random
More informationNearest Neighbor Decoding in MIMO Block-Fading Channels With Imperfect CSIR
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 58, NO. 3, MARCH 2012 1483 Nearest Neighbor Decoding in MIMO Block-Fading Channels With Imperfect CSIR A. Taufiq Asyhari, Student Member, IEEE, Albert Guillén
More informationMulti-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems
Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User
More informationIncremental Coding over MIMO Channels
Model Rateless SISO MIMO Applications Summary Incremental Coding over MIMO Channels Anatoly Khina, Tel Aviv University Joint work with: Yuval Kochman, MIT Uri Erez, Tel Aviv University Gregory W. Wornell,
More informationOn the Required Accuracy of Transmitter Channel State Information in Multiple Antenna Broadcast Channels
On the Required Accuracy of Transmitter Channel State Information in Multiple Antenna Broadcast Channels Giuseppe Caire University of Southern California Los Angeles, CA, USA Email: caire@usc.edu Nihar
More informationLecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH
: Theoretical Foundations of Wireless Communications 1 Wednesday, May 11, 2016 9:00-12:00, Conference Room SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 1 Overview
More informationSchur-convexity of the Symbol Error Rate in Correlated MIMO Systems with Precoding and Space-time Coding
Schur-convexity of the Symbol Error Rate in Correlated MIMO Systems with Precoding and Space-time Coding RadioVetenskap och Kommunikation (RVK 08) Proceedings of the twentieth Nordic Conference on Radio
More informationJoint Power Control and Beamforming Codebook Design for MISO Channels with Limited Feedback
Joint Power Control and Beamforming Codebook Design for MISO Channels with Limited Feedback Behrouz Khoshnevis and Wei Yu Department of Electrical and Computer Engineering University of Toronto, Toronto,
More informationFeasibility Conditions for Interference Alignment
Feasibility Conditions for Interference Alignment Cenk M. Yetis Istanbul Technical University Informatics Inst. Maslak, Istanbul, TURKEY Email: cenkmyetis@yahoo.com Tiangao Gou, Syed A. Jafar University
More informationChapter 3 Transformations
Chapter 3 Transformations An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Linear Transformations A function is called a linear transformation if 1. for every and 2. for every If we fix the bases
More informationLECTURE 18. Lecture outline Gaussian channels: parallel colored noise inter-symbol interference general case: multiple inputs and outputs
LECTURE 18 Last time: White Gaussian noise Bandlimited WGN Additive White Gaussian Noise (AWGN) channel Capacity of AWGN channel Application: DS-CDMA systems Spreading Coding theorem Lecture outline Gaussian
More informationReceived Signal, Interference and Noise
Optimum Combining Maximum ratio combining (MRC) maximizes the output signal-to-noise ratio (SNR) and is the optimal combining method in a maximum likelihood sense for channels where the additive impairment
More informationOn the Performance of. Golden Space-Time Trellis Coded Modulation over MIMO Block Fading Channels
On the Performance of 1 Golden Space-Time Trellis Coded Modulation over MIMO Block Fading Channels arxiv:0711.1295v1 [cs.it] 8 Nov 2007 Emanuele Viterbo and Yi Hong Abstract The Golden space-time trellis
More informationMultiple-Input Multiple-Output Systems
Multiple-Input Multiple-Output Systems What is the best way to use antenna arrays? MIMO! This is a totally new approach ( paradigm ) to wireless communications, which has been discovered in 95-96. Performance
More information672 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 2, FEBRUARY We only include here some relevant references that focus on the complex
672 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 2, FEBRUARY 2009 Ordered Eigenvalues of a General Class of Hermitian Rom Matrices With Application to the Performance Analysis of MIMO Systems Luis
More informationAdaptive Space-Time Shift Keying Based Multiple-Input Multiple-Output Systems
ACSTSK Adaptive Space-Time Shift Keying Based Multiple-Input Multiple-Output Systems Professor Sheng Chen Electronics and Computer Science University of Southampton Southampton SO7 BJ, UK E-mail: sqc@ecs.soton.ac.uk
More informationDiversity-Fidelity Tradeoff in Transmission of Analog Sources over MIMO Fading Channels
Diversity-Fidelity Tradeo in Transmission o Analog Sources over MIMO Fading Channels Mahmoud Taherzadeh, Kamyar Moshksar and Amir K. Khandani Coding & Signal Transmission Laboratory www.cst.uwaterloo.ca
More informationUnder sum power constraint, the capacity of MIMO channels
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL 6, NO 9, SEPTEMBER 22 242 Iterative Mode-Dropping for the Sum Capacity of MIMO-MAC with Per-Antenna Power Constraint Yang Zhu and Mai Vu Abstract We propose an
More informationPerformance Analysis of Multiple Antenna Systems with VQ-Based Feedback
Performance Analysis of Multiple Antenna Systems with VQ-Based Feedback June Chul Roh and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla,
More informationMulti-User Gain Maximum Eigenmode Beamforming, and IDMA. Peng Wang and Li Ping City University of Hong Kong
Multi-User Gain Maximum Eigenmode Beamforming, and IDMA Peng Wang and Li Ping City University of Hong Kong 1 Contents Introduction Multi-user gain (MUG) Maximum eigenmode beamforming (MEB) MEB performance
More informationQuantifying the Performance Gain of Direction Feedback in a MISO System
Quantifying the Performance Gain of Direction Feedback in a ISO System Shengli Zhou, Jinhong Wu, Zhengdao Wang 3, and ilos Doroslovacki Dept. of Electrical and Computer Engineering, University of Connecticut
More informationExploiting Quantized Channel Norm Feedback Through Conditional Statistics in Arbitrarily Correlated MIMO Systems
Exploiting Quantized Channel Norm Feedback Through Conditional Statistics in Arbitrarily Correlated MIMO Systems IEEE TRANSACTIONS ON SIGNAL PROCESSING Volume 57, Issue 10, Pages 4027-4041, October 2009.
More informationCapacity optimization for Rician correlated MIMO wireless channels
Capacity optimization for Rician correlated MIMO wireless channels Mai Vu, and Arogyaswami Paulraj Information Systems Laboratory, Department of Electrical Engineering Stanford University, Stanford, CA
More informationCapacity Analysis of MIMO Systems with Unknown Channel State Information
Capacity Analysis of MIMO Systems with Unknown Channel State Information Jun Zheng an Bhaskar D. Rao Dept. of Electrical an Computer Engineering University of California at San Diego e-mail: juzheng@ucs.eu,
More informationAnatoly Khina. Joint work with: Uri Erez, Ayal Hitron, Idan Livni TAU Yuval Kochman HUJI Gregory W. Wornell MIT
Network Modulation: Transmission Technique for MIMO Networks Anatoly Khina Joint work with: Uri Erez, Ayal Hitron, Idan Livni TAU Yuval Kochman HUJI Gregory W. Wornell MIT ACC Workshop, Feder Family Award
More informationInteractive Interference Alignment
Interactive Interference Alignment Quan Geng, Sreeram annan, and Pramod Viswanath Coordinated Science Laboratory and Dept. of ECE University of Illinois, Urbana-Champaign, IL 61801 Email: {geng5, kannan1,
More informationA New SLNR-based Linear Precoding for. Downlink Multi-User Multi-Stream MIMO Systems
A New SLNR-based Linear Precoding for 1 Downlin Multi-User Multi-Stream MIMO Systems arxiv:1008.0730v1 [cs.it] 4 Aug 2010 Peng Cheng, Meixia Tao and Wenjun Zhang Abstract Signal-to-leaage-and-noise ratio
More informationDS-GA 1002 Lecture notes 10 November 23, Linear models
DS-GA 2 Lecture notes November 23, 2 Linear functions Linear models A linear model encodes the assumption that two quantities are linearly related. Mathematically, this is characterized using linear functions.
More informationMinimum Feedback Rates for Multi-Carrier Transmission With Correlated Frequency Selective Fading
Minimum Feedback Rates for Multi-Carrier Transmission With Correlated Frequency Selective Fading Yakun Sun and Michael L. Honig Department of ECE orthwestern University Evanston, IL 60208 Abstract We consider
More informationA Coding Strategy for Wireless Networks with no Channel Information
A Coding Strategy for Wireless Networks with no Channel Information Frédérique Oggier and Babak Hassibi Abstract In this paper, we present a coding strategy for wireless relay networks, where we assume
More informationRandom Matrices and Wireless Communications
Random Matrices and Wireless Communications Jamie Evans Centre for Ultra-Broadband Information Networks (CUBIN) Department of Electrical and Electronic Engineering University of Melbourne 3.5 1 3 0.8 2.5
More informationCapacity Pre-Log of SIMO Correlated Block-Fading Channels
Capacity Pre-Log of SIMO Correlated Block-Fading Channels Wei Yang, Giuseppe Durisi, Veniamin I. Morgenshtern, Erwin Riegler 3 Chalmers University of Technology, 496 Gothenburg, Sweden ETH Zurich, 809
More informationCommunications over the Best Singular Mode of a Reciprocal MIMO Channel
Communications over the Best Singular Mode of a Reciprocal MIMO Channel Saeed Gazor and Khalid AlSuhaili Abstract We consider two nodes equipped with multiple antennas that intend to communicate i.e. both
More informationON BEAMFORMING WITH FINITE RATE FEEDBACK IN MULTIPLE ANTENNA SYSTEMS
ON BEAMFORMING WITH FINITE RATE FEEDBACK IN MULTIPLE ANTENNA SYSTEMS KRISHNA KIRAN MUKKAVILLI ASHUTOSH SABHARWAL ELZA ERKIP BEHNAAM AAZHANG Abstract In this paper, we study a multiple antenna system where
More informationPOWER ALLOCATION AND OPTIMAL TX/RX STRUCTURES FOR MIMO SYSTEMS
POWER ALLOCATION AND OPTIMAL TX/RX STRUCTURES FOR MIMO SYSTEMS R. Cendrillon, O. Rousseaux and M. Moonen SCD/ESAT, Katholiee Universiteit Leuven, Belgium {raphael.cendrillon, olivier.rousseaux, marc.moonen}@esat.uleuven.ac.be
More informationMorning Session Capacity-based Power Control. Department of Electrical and Computer Engineering University of Maryland
Morning Session Capacity-based Power Control Şennur Ulukuş Department of Electrical and Computer Engineering University of Maryland So Far, We Learned... Power control with SIR-based QoS guarantees Suitable
More informationA robust transmit CSI framework with applications in MIMO wireless precoding
A robust transmit CSI framework with applications in MIMO wireless precoding Mai Vu, and Arogyaswami Paulraj Information Systems Laboratory, Department of Electrical Engineering Stanford University, Stanford,
More informationUpper Bounds on MIMO Channel Capacity with Channel Frobenius Norm Constraints
Upper Bounds on IO Channel Capacity with Channel Frobenius Norm Constraints Zukang Shen, Jeffrey G. Andrews, Brian L. Evans Wireless Networking Communications Group Department of Electrical Computer Engineering
More informationCharacterizing the Capacity for MIMO Wireless Channels with Non-zero Mean and Transmit Covariance
Characterizing the Capacity for MIMO Wireless Channels with Non-zero Mean and Transmit Covariance Mai Vu and Arogyaswami Paulraj Information Systems Laboratory, Department of Electrical Engineering Stanford
More informationWideband Fading Channel Capacity with Training and Partial Feedback
Wideband Fading Channel Capacity with Training and Partial Feedback Manish Agarwal, Michael L. Honig ECE Department, Northwestern University 145 Sheridan Road, Evanston, IL 6008 USA {m-agarwal,mh}@northwestern.edu
More informationChannel. Feedback. Channel
Space-time Transmit Precoding with Imperfect Feedback Eugene Visotsky Upamanyu Madhow y Abstract The use of channel feedback from receiver to transmitter is standard in wireline communications. While knowledge
More informationCapacity of Block Rayleigh Fading Channels Without CSI
Capacity of Block Rayleigh Fading Channels Without CSI Mainak Chowdhury and Andrea Goldsmith, Fellow, IEEE Department of Electrical Engineering, Stanford University, USA Email: mainakch@stanford.edu, andrea@wsl.stanford.edu
More informationDiversity Combining Techniques
Diversity Combining Techniques When the required signal is a combination of several plane waves (multipath), the total signal amplitude may experience deep fades (Rayleigh fading), over time or space.
More informationLecture Notes 1: Vector spaces
Optimization-based data analysis Fall 2017 Lecture Notes 1: Vector spaces In this chapter we review certain basic concepts of linear algebra, highlighting their application to signal processing. 1 Vector
More informationOptimal Signal Constellations for Fading Space-Time Channels
Optimal Signal Constellations for Fading Space-Time Channels 1. Space-time channels Alfred O. Hero University of Michigan - Ann Arbor Outline 2. Random coding exponent and cutoff rate 3. Discrete K-dimensional
More information