Signal Processing Advances in Wireless and Mobile Communications, Volume 1

Signal Processing Advances in Wireless and Mobile Communications, Volume 1 : Trends in Channel Estimation and Equalization

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This is the first in a two-volume set that captures major recent advances in signal processing (SP) tools, as they apply to wireless and mobile communication systems. The set brings together contributions by leaders in the field worldwide, and covers an exceptionally wide range of technologies and methodologies, including noise and interference cancellation, modem design, mobile Internet services, next-generation audio/video broadcasting, cellular telephony, and wireless multimedia networks.As information-bearing signals propagate through fading media, modern equalizers must account for the variability of the mobile wireless channel, mitigate inter-symbol and co-channel interference and suppress noise at the single- or multi-sensor receivers. This book presents the latest bandwidth-saving (semi-) blind algorithms and performance analysis, as well as linear precoding techniques that capitalize on transmit-redundancy to offer distinct improvements over training-based systems. Coverage includes: subspace methods for blind identification and deconvolution; blind identification and equalization of channels driven by colored signals; optimum subspace methods; linear prediction algorithms for multichannel equalization; semi-blind methods for FIR multichannel estimation; blind decision-feedback equalization; and more

Product details

  • Paperback | 448 pages
  • 185.2 x 243.8 x 24.4mm | 895.77g
  • Pearson Education (US)
  • Prentice Hall
  • Upper Saddle River, United States
  • English
  • 0130271624
  • 9780130271624

Table of contents

Preface. 1. Channel Estimation And Equalization Using Higher-Order Statistics. Introduction. Single-User Systems: Baud Rate Sampling. Single-User Systems: Fractional Sampling. Multi-user Systems. Concluding Remarks. Bibliography.2. Performance Bound For Blind Channel Estimation. Introduction. Problem Statement and Preliminaries. CRB for Constrained Estimates. CRB for Estimates of Invariants. CRB for Projection Errors. Numerical Examples. Concluding Remarks. Appendix 2:.A Proof of Proposition 2. Bibliography.3. Ub Pace Method For Blind Identification And Deconvolution. Introduction. Subspace Identification of IMO Channels. Subspace Identification of MIMO Channels. Applications to the Blind Channel Estimation of CDMA Systems. Undermodeled Channel Identification. Appendix 3:.A. Bibliography.4. Blind Identification And Equalization Of Channels Driven By Colored Signals. Introduction. FIR MIMO Channel. Identifiability Using O. Blind Identification via Decorrelation. Final Remarks. Bibliography.5. Optimum Subspace Method. Introduction. Data Model and Notations. Subspace Ideas and Notations. Parameterizations. Estimation Procedure. Statistical Analysis. Relation to Direction Estimation. Further Results for the Noise Subspace Parameterization. Simulation Examples. Conclusions. Appendix 5:.A. Bibliography.6. Linear Predictive Algorithm For Blind Multichannel Identification. Introduction. Channel Identification Based on Second Order Statistics: Problem Formulation. Linear Prediction Algorithm for Channel Identification. Outer-Product Decomposition Algorithm. Multi-step Linear Prediction. Channel Estimation by Linear Smoothing (Not Predicting). Channel Estimation by Constrained Output Energy Minimization. Discussion. Simulation Results. Summary. Bibliography.7. Semi-Blind Method For Fir Multichannel Estimation. Introduction. Problem Formulation. Classification of Semi-Blind Methods. Identifiability Conditions for Semi-Blind Channel Estimation. Performance Measure: Cramer-Rao Bounds. Performance Optimization Issues. Optimal Semi-Blind Methods. Blind DML. Three Suboptimal DML Based Semi-Blind Criteria. Semi-Blind Criteria as a Combination of a Blind and a T Based Criteria. Performance of Semi-Blind Quadratic Criteria. Gaussian Methods. Conclusion. Bibliography more

About Georgios B. Giannakis

DR. GEORGIOS GIANNAKIS is Professor in the Electrical Engineering Department at the University of Minnesota. Giannakis' research interests include signal processing, communications, time series analysis, estimation and detection theory, and system identification. His specific areas of expertise include (poly)spectral analysis, wavelets, and cyclostationary and non-Gaussian signal processing for sonar, SAR, array and image processing applications. Formerly Professor in the Communications, Control, and Signal Processing Laboratory at the University of Virginia, he holds a Ph.D. in electrical engineering from the University of Southern California, Los more