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Random Matrix Methods for Wireless Communications

作者: Romain Couillet; Merouane Debbah
出版商: Cambridge : Cambridge University Press, 2011.
版本/格式:   電子書 : 文獻 : 英語所有版本和格式的總覽
資料庫:WorldCat
提要:
An introduction to random matrix theory and its applications to real-world problems in signal processing and wireless communications.
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類型/形式: Electronic books
其他的實體格式: Print version:
Couillet, Romain.
Random Matrix Methods for Wireless Communications.
Cambridge : Cambridge University Press, ©2011
資料類型: 文獻, 網際網路資源
文件類型: 網路資源, 電腦資料
所有的作者/貢獻者: Romain Couillet; Merouane Debbah
ISBN: 9781139138055 1139138057 9781139145381 113914538X 9780511994746 0511994745 9781139142076 1139142070
OCLC系統控制編碼: 763157919
注意: 12.3.1 Orthogonal CDMA in uplink frequency flat channels.
描述: 1 online resource (563 pages)
内容: Cover; Title; Copyright; Dedication; Contents; Preface; Acknowledgments; Acronyms; Notation; 1 Introduction; 1.1 Motivation; 1.2 History and book outline; Part I Theoretical aspects; 2 Random matrices; 2.1 Small dimensional random matrices; 2.1.1 Definitions and notations; 2.1.2 Wishart matrices; 2.2 Large dimensional random matrices; 2.2.1 Why go to infinity?; 2.2.2 Limit spectral distributions; 3 The Stieltjes transform method; 3.1 Definitions and overview; 3.2 The Marcenko-Pastur law; 3.2.1 Proof of the Marcenko-Pastur law; 3.2.2 Truncation, centralization, and rescaling. 3.3 Stieltjes transform for advanced models3.4 Tonelli theorem; 3.5 Central limit theorems; 4 Free probability theory; 4.1 Introduction to free probability theory; 4.2 R- and S-transforms; 4.3 Free probability and random matrices; 4.4 Free probability for Gaussian matrices; 4.5 Free probability for Haar matrices; 5 Combinatoric approaches; 5.1 The method of moments; 5.2 Free moments and cumulants; 5.3 Generalization to more structured matrices; 5.4 Free moments in small dimensional matrices; 5.5 Rectangular free probability; 5.6 Methodology; 6 Deterministic equivalents. 6.1 Introduction to deterministic equivalents6.2 Techniques for deterministic equivalents; 6.2.1 Bai and Silverstein method; 6.2.2 Gaussian method; 6.2.3 Information plus noise models; 6.2.4 Models involving Haar matrices; 6.3 A central limit theorem; 7 Spectrum analysis; 7.1 Sample covariance matrix; 7.1.1 No eigenvalues outside the support; 7.1.2 Exact spectrum separation; 7.1.3 Asymptotic spectrum analysis; 7.2 Information plus noise model; 7.2.1 Exact separation; 7.2.2 Asymptotic spectrum analysis; 8 Eigen-inference; 8.1 G-estimation; 8.1.1 Girko G-estimators. 8.1.2 G-estimation of population eigenvalues and eigenvectors8.1.3 Central limit for G-estimators; 8.2 Moment deconvolution approach; 9 Extreme eigenvalues; 9.1 Spiked models; 9.1.1 Perturbed sample covariance matrix; 9.1.2 Perturbed random matrices with invariance properties; 9.2 Distribution of extreme eigenvalues; 9.2.1 Introduction to the method of orthogonal polynomials; 9.2.2 Limiting laws of the extreme eigenvalues; 9.3 Random matrix theory and eigenvectors; 10 Summary and partial conclusions; Part II Applications to wireless communications. 11 Introduction to applications in telecommunications11.1 Historical account of major results; 11.1.1 Rate performance of multi-dimensional systems; 11.1.2 Detection and estimation in large dimensional systems; 11.1.3 Random matrices and flexible radio; 12 System performance of CDMA technologies; 12.1 Introduction; 12.2 Performance of random CDMA technologies; 12.2.1 Random CDMA in uplink frequency flat channels; 12.2.2 Random CDMA in uplink frequency selective channels; 12.2.3 Random CDMA in downlink frequency selective channels; 12.3 Performance of orthogonal CDMA technologies.
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An introduction to random matrix theory and its applications to real-world problems in signal processing and wireless communications.  再讀一些...

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