Signal Processing and Optimization for Transceiver Systems

Presenting the first complete treatment of MIMO transceiver optimization, this self-contained book provides all the mathematical information needed to understand transceiver optimization in a single volume. It begins with a review of digital communication fundamentals, and then moves on to a detailed study of joint transceiver optimization, starting from simple single-input single-output channels all the way to minimum bit error rate transceivers for MIMO channels. Crucial background material is covered, such as Schur convex functions, matrix calculus, and constrained optimization, together with eight appendices providing further background material on topics such as matrix theory, random processes, and sampling theory. A final ninth appendix provides a grand summary of all the optimization results. With 360 illustrations, over 70 worked examples, and numerous summary tables provided to aid understanding of key concepts, this book is ideal for graduate students, practitioners, and researchers in the fields of communications and signal processing.

Spis treści:
Part I. Communication Fundamentals
1. Introduction
2. Review of basic ideas from digital communication
3. Digital communication systems and filter banks
4. Discrete time representations
5. Classical transceiver techniques
6. Channel capacity
7. Channel equalization with transmitter redundancy
8. The lazy precoder with a zero-forcing equalizer
Part II. Transceiver Optimization
9. History and outline
10. Single-input single-output transceiver optimization
11. Optimal transceivers for diagonal channels
12. MMSE transceivers with zero-forcing equalizers
13. MMSE transceivers without zero forcing
14. Bit allocation and power minimization
15. Transceivers with orthonormal precoders
16. Minimization of error probability in transceivers
17. Optimization of cyclic prefix transceivers
18. Optimization of zero padded systems
19. Transceivers with decision feedback equalizers
Part III. Mathematical Background
20. Matrix differentiation
21. Convexity, Schur convexity and majorization theory
22. Optimization with equality and inequality constraints
Part IV. Appendices, A. Inner products, norms, and inequalities
B. Matrices, a brief overview
C. Singular value decomposition
D. Properties of pseudocirculant matrices
E. Random processes
F. Wiener filtering
G. Review of concepts from sampling theory
H. Euclids algorithm
I. Transceiver optimization
Summary and tables
Glossary and acronyms
Bibliography.