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Random Matrix Methods for Wireless Communications [Hardback]

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  • Formāts: Hardback, 562 pages, height x width x depth: 249x175x30 mm, weight: 1160 g, 8 Tables, black and white; 91 Line drawings, unspecified
  • Izdošanas datums: 29-Sep-2011
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107011639
  • ISBN-13: 9781107011632
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  • Cena: 124,94 €
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Random Matrix Methods for Wireless CommunicationsPalielināt
  • Formāts: Hardback, 562 pages, height x width x depth: 249x175x30 mm, weight: 1160 g, 8 Tables, black and white; 91 Line drawings, unspecified
  • Izdošanas datums: 29-Sep-2011
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107011639
  • ISBN-13: 9781107011632
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781107011632.html
Keywords:
"Blending theoretical results with practical applications, this book provides an introduction to random matrix theory and shows how it can be used to tackle a variety of problems in wireless communications. The Stieltjes transform method, free probability theory, combinatoric approaches, deterministic equivalents and spectral analysis methods for statistical inference are all covered from a unique engineering perspective. Detailed mathematical derivations are presented throughout, with thorough explanation of the key results and all fundamental lemmas required for the reader to derive similar calculus on their own. These core theoretical concepts are then applied to a wide range of real-world problems in signal processing and wireless communications, including performance analysis of CDMA, MIMO and multi-cell networks, as well as signal detection and estimation in cognitive radio networks. The rigorous yet intuitive style helps demonstrate to students and researchers alike how to choose the correct approach for obtaining mathematically accurate results"--

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Papildus informācija

An introduction to random matrix theory and its applications to real-world problems in signal processing and wireless communications.
Preface xiii
Acknowledgments xv
Acronyms xvi
Notation xviii
1 Introduction
1(14)
1.1 Motivation
1(5)
1.2 History and book outline
6(9)
PART I Theoretical aspects
15(234)
2 Random matrices
17(18)
2.1 Small dimensional random matrices
17(12)
2.1.1 Definitions and notations
17(2)
2.1.2 Wishart matrices
19(10)
2.2 Large dimensional random matrices
29(6)
2.2.1 Why go to infinity?
29(1)
2.2.2 Limit spectral distributions
30(5)
3 The Stieltjes transform method
35(36)
3.1 Definitions and overview
35(7)
3.2 The Marcenko-Pastur law
42(15)
3.2.1 Proof of the Marcenko Pastur law
44(10)
3.2.2 Truncation, centralization, and rescaling
54(3)
3.3 Stieltjes transform for advanced models
57(4)
3.4 Tonelli theorem
61(2)
3.5 Central limit theorems
63(8)
4 Free probability theory
71(24)
4.1 Introduction to free probability theory
72(3)
4.2 R- and S-transforms
75(2)
4.3 Free probability and random matrices
77(7)
4.4 Free probability for Gaussian matrices
84(3)
4.5 Free probability for Haar matrices
87(8)
5 Combinatoric approaches
95(18)
5.1 The method of moments
95(3)
5.2 Free moments and cumulants
98(7)
5.3 Generalization to more structured matrices
105(3)
5.4 Free moments in small dimensional matrices
108(1)
5.5 Rectangular free probability
109(2)
5.6 Methodology
111(2)
6 Deterministic equivalents
113(66)
6.1 Introduction to deterministic equivalents
113(2)
6.2 Techniques for deterministic equivalents
115(60)
6.2.1 Bai and Silverstein method
115(24)
6.2.2 Gaussian method
139(6)
6.2.3 Information plus noise models
145(8)
6.2.4 Models involving Haar matrices
153(22)
6.3 A central limit theorem
175(4)
7 Spectrum analysis
179(20)
7.1 Sample covariance matrix
180(12)
7.1.1 No eigenvalues outside the support
180(3)
7.1.2 Exact spectrum separation
183(3)
7.1.3 Asymptotic spectrum analysis
186(6)
7.2 Information plus noise model
192(7)
7.2.1 Exact separation
192(3)
7.2.2 Asymptotic spectrum analysis
195(4)
8 Eigen-inference
199(24)
8.1 G-estimation
199(19)
8.1.1 Girko G-estimators
199(2)
8.1.2 G-estimation of population eigenvalues and eigenvectors
201(12)
8.1.3 Central limit for G-estimators
213(5)
8.2 Moment deconvolution approach
218(5)
9 Extreme eigenvalues
223(20)
9.1 Spiked models
223(7)
9.1.1 Perturbed sample covariance matrix
224(4)
9.1.2 Perturbed random matrices with invariance properties
228(2)
9.2 Distribution of extreme eigenvalues
230(7)
9.2.1 Introduction to the method of orthogonal polynomials
230(3)
9.2.2 Limiting laws of the extreme eigenvalues
233(4)
9.3 Random matrix theory and eigenvectors
237(6)
10 Summary and partial conclusions
243(6)
PART II Applications to wireless communications
249(266)
11 Introduction to applications in telecommunications
251(12)
11.1 Historical account of major results
251(12)
11.1.1 Rate performance of multi-dimensional systems
252(4)
11.1.2 Detection and estimation in large dimensional systems
256(3)
11.1.3 Random matrices and flexible radio
259(4)
12 System performance of CDMA technologies
263(30)
12.1 Introduction
263(1)
12.2 Performance of random CDMA technologies
264(20)
12.2.1 Random CDMA in uplink frequency flat channels
264(9)
12.2.2 Random CDMA in uplink frequency selective channels
273(8)
12.2.3 Random CDMA in downlink frequency selective channels
281(3)
12.3 Performance of orthogonal CDMA technologies
284(9)
12.3.1 Orthogonal CDMA in uplink frequency flat channels
285(1)
12.3.2 Orthogonal CDMA in uplink frequency selective channels
285(1)
12.3.3 Orthogonal CDMA in downlink frequency selective channels
286(7)
13 Performance of multiple antenna systems
293(42)
13.1 Quasi-static MIMO fading channels
293(2)
13.2 Time-varying Rayleigh channels
295(5)
13.2.1 Small dimensional analysis
296(1)
13.2.2 Large dimensional analysis
297(1)
13.2.3 Outage capacity
298(2)
13.3 Correlated frequency flat fading channels
300(16)
13.3.1 Communication in strongly correlated channels
305(4)
13.3.2 Ergodic capacity in strongly correlated channels
309(2)
13.3.3 Ergodic capacity in weakly correlated channels
311(1)
13.3.4 Capacity maximizing precoder
312(4)
13.4 Rician flat fading channels
316(6)
13.4.1 Quasi-static mutual information and ergodic capacity
316(2)
13.4.2 Capacity maximizing power allocation
318(2)
13.4.3 Outage mutual information
320(2)
13.5 Frequency selective channels
322(6)
13.5.1 Ergodic capacity
324(1)
13.5.2 Capacity maximizing power allocation
325(3)
13.6 Transceiver design
328(7)
13.6.1 Channel matrix model with i.i.d. entries
331(1)
13.6.2 Channel matrix model with generalized variance profile
332(3)
14 Rate performance in multiple access and broadcast channels
335(34)
14.1 Broadcast channels with linear precoders
336(19)
14.1.1 System model
339(2)
14.1.2 Deterministic equivalent of the SINR
341(7)
14.1.3 Optimal regularized zero-forcing precoding
348(1)
14.1.4 Zero-forcing precoding
349(4)
14.1.5 Applications
353(2)
14.2 Rate region of MIMO multiple access channels
355(14)
14.2.1 MAC rate region in quasi-static channels
357(3)
14.2.2 Ergodic MAC rate region
360(4)
14.2.3 Multi-user uplink sum rate capacity
364(5)
15 Performance of multi-cellular and relay networks
369(24)
15.1 Performance of multi-cell networks
369(9)
15.1.1 Two-cell network
373(3)
15.1.2 Wyner model
376(2)
15.2 Multi-hop communications
378(15)
15.2.1 Multi-hop model
379(3)
15.2.2 Mutual information
382(1)
15.2.3 Large dimensional analysis
382(6)
15.2.4 Optimal transmission strategy
388(5)
16 Detection
393(28)
16.1 Cognitive radios and sensor networks
393(3)
16.2 System model
396(3)
16.3 Neyman-Pearson criterion
399(13)
16.3.1 Known signal and noise variances
400(6)
16.3.2 Unknown signal and noise variances
406(1)
16.3.3 Unknown number of sources
407(5)
16.4 Alternative signal sensing approaches
412(9)
16.4.1 Condition number method
413(1)
16.4.2 Generalized likelihood ratio test
414(2)
16.4.3 Test power and error exponents
416(5)
17 Estimation
421(56)
17.1 Directions of arrival
422(10)
17.1.1 System model
422(1)
17.1.2 The MUSIC approach
423(2)
17.1.3 Large dimensional eigen-inference
425(4)
17.1.4 The correlated signal case
429(3)
17.2 Blind multi-source localization
432(45)
17.2.1 System model
434(2)
17.2.2 Small dimensional inference
436(2)
17.2.3 Conventional large dimensional approach
438(2)
17.2.4 Free deconvolution approach
440(7)
17.2.5 Analytic method
447(22)
17.2.6 Joint estimation of number of users, antennas and powers
469(2)
17.2.7 Performance analysis
471(6)
18 System modeling
477(24)
18.1 Introduction to Bayesian channel modeling
478(2)
18.2 Channel modeling under environmental uncertainty
480(21)
18.2.1 Channel energy constraints
481(3)
18.2.2 Spatial correlation models
484(17)
19 Perspectives
501(10)
19.1 From asymptotic results to finite dimensional studies
501(4)
19.2 The replica method
505(1)
19.3 Towards time-varying random matrices
506(5)
20 Conclusion
511(4)
References 515(22)
Index 537
Romain Couillet is an Assistant Professor at the Chair on System Sciences and the Energy Challenge at Supélec, France. Previously he was an Algorithm Development Engineer for ST-Ericsson and he received his Ph.D. from Supélec in 2010. Mérouane Debbah is a Professor at Supélec, where he holds the AlcatelLucent Chair on Flexible Radio. He is the recipient of several awards, including the 2007 General Symposium IEEE Globecom best paper award and the Wi-Opt 2009 best paper award.