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By Claude Oestges

  • “This ebook bargains very important insights into how space-time coding should be adapted for real-world MIMO channels. The dialogue of MIMO propagation versions can also be intuitive and good developed.” Professor Arogyaswami J. Paulraj, Stanford college, CA “Finally a e-book dedicated to MIMO from a brand new point of view that bridges the limits among propagation, channel modeling, sign processing and space-time coding. it really is of excessive reference price, combining intuitive and conceptual causes with precise, stringent derivations of simple evidence of MIMO.” Ernst Bonek, Emeritus Professor, Technische Universität Wien, Austria

List of figures

, Pages xi-xvi
List of tables

, Page xvii

, Pages xix-xx
List of abbreviations

, Pages xxi-xxiii
List of symbols

, Pages xxv-xxvi
About the authors

, Page xxvii
1 - advent to multi-antenna communications

, Pages 1-27
2 - actual MIMO channel modeling

, Pages 29-71
3 - Analytical MIMO channel representations for procedure design

, Pages 73-107
4 - Mutual info and means of real-world random MIMO channels

, Pages 109-153
5 - Space—time coding over i.i.d. rayleigh flat fading channels

, Pages 155-222
6 - mistakes likelihood in real-world MIMO channels

, Pages 223-273
7 - Space—time coding over real-world MIMO channels without transmit channel knowledge

, Pages 275-317
8 - Space—time coding with partial transmit channel knowledge

, Pages 319-368
9 - Space—time coding for frequency selective channels

, Pages 369-402
Appendix A - helpful mathematical and matrix properties

, Pages 403-404
Appendix B - complicated gaussian random variables and matrices

, Pages 405-408
Appendix C - Stanford college intervening time channel models

, Pages 409-410
Appendix D - Antenna coupling model

, Pages 411-416
Appendix E - Derivation of the typical pairwise errors probability

, Pages 417-422

, Pages 423-443

, Pages 445-448

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Additional info for MIMO Wireless Communications. From real-world propagation to space—time code design

Example text

H2nt (t, τ) ⎥ ⎥ ⎥, .. ⎦ . 32) . . 33) (n) pt and pr designating the locations of the mth transmit antenna and the nth receive antenna. 33) is written for isotropic antennas at both ends. 5). Finally, let us introduce Ht [τ] H(t, τ) as a compact alternative notation for the MIMO channel matrix ns −1 H(t, τ) = Ht [τ] = H (t, τk ) δ (τ − τk ) . 34) k=0 This compact notation will be used when we describe design methods for space-time codes over frequency-selective channels. Introducing the steering vectors Under three assumptions (the narrowband and balanced array assumptions as well as the plane wave assumption [PNG03, Her04]), we will now show how hnm (t, τ) can be easily written as a function of h11 t, τ, t , r .

Analogous to the MISO case, consider that two symbols c1 and c2 are transmitted simultaneously from transmit antennas 1 and 2 during the first symbol period, while symbols −c2∗ and c1∗ are transmitted from antennas 1 and 2 during the next symbol period. 70) Note that the subscripts here denote the receive and transmit antenna index and not the symbol period. 72) where n1 and n2 are the additive noise contributions at each symbol period over the receive antenna array (so the subscripts here denote the symbol periods, and not the antennas).

As a consequence, time-variant directional channels can be considered as random variables, so that the transfer functions become stochastic processes. Their statistical characterization necessitates the knowledge of their multidimensional joint probability density functions. This would require a great deal of effort, which is unlikely to happen in practice. A more convenient approach consists of modeling only the multidimensional correlation functions. 7) while the Doppler-delay transmit-receive direction correlation is outlined by RS ν, ν , τ, τ , t, t, r, r = E S ν, τ, t, r , S∗ ν , τ , t, r .

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