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Adaptive Filters for Blind Equalization 24.1 Introduction 24.2 Channel Equalization in QAM Data Communication Systems 24.3 Decision-Directed Adaptive Channel Equalizer 24.4 Basic Facts on Blind Adaptive Equalization 24.5 Adaptive Algorithms and Notations 24.6 Mean Cost Functions and Associated Algorithms The Sato Algorithm • BGR Extensions of Sato Algorithm • Constant Modulus or Godard Algorithms • Stop-and-Go Algorithms • Shalvi and Weinstein Algorithms • Summary A Common Analysis Approach • Local Convergence of Blind Equalizers • Initialization Issues Linearly Constrained Equalizer With Convex Cost 24.7 Initialization and Convergence of Blind Equalizers 24.8 Globally Convergent Equalizers 24.9 Fractionally Spaced Blind Equalizers 24.10 Concluding Remarks References Zhi. | Zhi Ding. Adaptive Filters for Blind Equalization. 2000 CRC Press LLC. http www.engnetbase.com . Adaptive Filters for Blind Equalization Zhi Ding Auburn University 24.1 Introduction 24.2 Channel Equalization in QAM Data Communication Systems 24.3 Decision-Directed Adaptive Channel Equalizer 24.4 Basic Facts on Blind Adaptive Equalization 24.5 Adaptive Algorithms and Notations 24.6 Mean Cost Functions and Associated Algorithms The Sato Algorithm BGR Extensions of Sato Algorithm Constant Modulus or Godard Algorithms Stop-and-Go Algorithms Shalvi and Weinstein Algorithms Summary 24.7 Initialization and Convergence of Blind Equalizers A Common Analysis Approach Local Convergence of Blind Equalizers Initialization Issues 24.8 Globally Convergent Equalizers Linearly Constrained Equalizer With Convex Cost 24.9 Fractionally Spaced Blind Equalizers 24.10 Concluding Remarks References 24.1 Introduction One of the earliest and most successful applications of adaptive filters is adaptive channel equalization in digital communication systems. Using the standard least mean LMS algorithm an adaptive equalizer is a finite-impulse-response FIR filter whose desired reference signal is a known training sequence sent by the transmitter over the unknown channel. The reliance of an adaptive channel equalizer on a training sequence requires that the transmitter cooperates by often periodically resending the training sequence lowering the effective data rate of the communication link. In many high-data-rate bandlimited digital communication systems the transmission of a training sequence is either impractical or very costly in terms of data throughput. Conventional LMS adaptive filters depending on the use of training sequences cannot be used. For this reason blind adaptive channel equalization algorithms that do not rely on training signals have been developed. Using these blind algorithms individual receivers can begin self-adaptation without transmitter assistance. This ability of .
