عنوان

MIMO transceiver design via majorization theory /

1282822993

1601980310

9781282822993

9781601980311

9781601980304

b745676

MIMO transceiver design via majorization theory /

[Book]

Daniel P. Palomar, Yi Jiang.

Hanover, MA :

Now Publishers,

©2007.

1 online resource (ix, 224 pages) :

illustrations

Foundations and trends in communications and information theory,

v. 3, issue 4-5

1567-2328 ;

"This book is originally published as Foundations and trends in communications and information theory, volume 3, issues 4-5 (2006)"--Page 4 of cover.

Includes bibliographical references.

Cover13; -- Contents -- Introduction -- MIMO Channels -- MIMO Communication Systems -- A First Glimpse at Linear Transceivers: Beamforming -- Historical Perspective on MIMO Transceivers -- Outline -- Majorization Theory -- Basic Definitions -- Basic Results -- Multiplicative Majorization -- Linear MIMO Transceivers -- System Model -- Problem Formulation -- Optimum Linear Receiver -- Optimum Linear Transmitter with Schur-Convex/Concave Cost Functions -- Optimum Linear Transmitter with Individual QoS Constraints -- Optimum Linear Transmitter with Arbitrary Cost Functions -- Extension to Multicarrier Systems -- Summary -- Appendix: Characterization of BER Functionfor QAM Constellations -- Appendix: Optimum Left Singular Vectors of P -- Appendix: Optimum Ordering of Eigenvalues -- Appendix: Proofs of Schur-Concavity/Convexity Lemmas -- Appendix: Waterfilling Algorithms -- Nonlinear Decision Feedback MIMO Transceivers -- System Model -- Problem Formulation -- Optimum Decision Feedback Receiver -- Optimum Transmitter with Global Measure of Performance -- Optimum Transmitter with Individual QoS Constraints -- A Dual Form Based on Dirty Paper Coding -- A Particular Case: CDMA Sequence Design -- Summary -- Appendix: Mutual Information and Wiener Filter -- Appendix: Proof of Lemma 4.1 -- Appendix: Proof of Theorem 4.3 -- Appendix: Proof of Procedure in Table 4.1 -- Appendix: Proof of Power Allocation for QoS -- Extensions and Future Lines of Research -- Multiuser Systems -- Robust Designs for Imperfect CSI -- ML Decoding -- Information-Theoretic Approach -- Convex Optimization Theory -- Convex Problems -- Classes of Convex Problems -- Reformulating a Problem in Convex Form -- Lagrange Duality Theory and KKT Optimality Conditions -- Sensitivity Analysis -- Efficient Numerical Algorithms to Solve Convex Problems -- Primal and Dual Decompositions -- Matrix Results -- Generalized Triangular Decompositions -- Miscellaneous Matrix Results -- Acknowledgments -- References.

0

Multiple-input multiple-output (MIMO) channels provide an abstract and unified representation of different physical communication systems, ranging from multi-antenna wireless channels to wireless digital subscriber line systems. They have the key property that several data streams can be simultaneously established. In general, the design of communication systems for MIMO channels is quite involved (if one can assume the use of sufficiently long and good codes, then the problem formulation simplifies drastically). The first difficulty lies on how to measure the global performance of such systems given the tradeoff on the performance among the different data streams. Once the problem formulation is defined, the resulting mathematical problem is typically too complicated to be optimally solved as it is a matrix-valued nonconvex optimization problem. This design problem has been studied for the past three decades (the first papers dating back to the 1970s) motivated initially by cable systems and more recently by wireless multi-antenna systems. The approach was to choose a specific global measure of performance and then to design the system accordingly, either optimally or suboptimally, depending on the difficulty of the problem. This text presents an up-to-date unified mathematical framework for the design of point-to-point MIMO transceivers with channel state information at both sides of the link according to an arbitrary cost function as a measure of the system performance. In addition, the framework embraces the design of systems with given individual performance on the data streams. Majorization theory is the underlying mathematical theory on which the framework hinges. It allows the transformation of the originally complicated matrix-valued nonconvex problem into a simple scalar problem. In particular, the additive majorization relation plays a key role in the design of linear MIMO transceivers (i.e., a linear precoder at the transmitter and a linear equalizer at the receiver), whereas the multiplicative majorization relation is the basis for nonlinear decision-feedback MIMO transceivers (i.e., a linear precoder at the transmitter and a decision-feedback equalizer at the receiver).

01251032

MIMO transceiver design via majorization theory.

1601980302

MIMO systems-- Design-- Mathematics.

Radio-- Transmitter-receivers-- Design-- Mathematics.

TECHNOLOGY & ENGINEERING-- Mobile & Wireless Communications.

TECHNOLOGY & ENGINEERING-- Radio.

TEC-- 034000

TEC-- 061000

TJK

Palomar, Daniel P.

Jiang, Yi,1978-

20201204055710.0

pn

[Book]

Y