## Adaptive and Iterative Signal Processing in CommunicationsAdaptive signal processing (ASP) and iterative signal processing (ISP) are important techniques in improving receiver performance in communication systems. Using examples from practical transceiver designs, this 2006 book describes the fundamental theory and practical aspects of both methods, providing a link between the two where possible. The first two parts of the book deal with ASP and ISP respectively, each in the context of receiver design over intersymbol interference (ISI) channels. In the third part, the applications of ASP and ISP to receiver design in other interference-limited channels, including CDMA and MIMO, are considered; the author attempts to illustrate how the two techniques can be used to solve problems in channels that have inherent uncertainty. Containing illustrations and worked examples, this book is suitable for graduate students and researchers in electrical engineering, as well as practitioners in the telecommunications industry. |

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### Contents

Channel equalization for dispersive channels | 15 |

Sequence detection with adaptive channel estimation | 48 |

Tables | 53 |

Estimation and detection for fading multipath channels | 75 |

Iterative signal processing for ISI channels | 105 |

Iterative receivers over static ISI channels | 134 |

Iterative receivers under timevarying channel conditions | 171 |

CDMA systems and multiuser detection | 195 |

Iterative CDMA receivers | 219 |

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### Common terms and phrases

adaptive antennas approach approximation assume BCJR algorithm becomes binary Bit error rate bit interleaver CDMA CDMA systems channel decoder channel estimation chapter consider convergence convolutional code convolutional encoder data sequence data symbols denotes EM algorithm error rate performance estimate of h EXIT chart fading channel Figure Gaussian random variable given GS iteration Hamming distance IEEE Trans input interfering signals ISI channel iterative receiver Kalman filter likelihood function linear LMMSE estimate LMS algorithm M-step MAP decoder MAP equalizer MAP symbol detection MIMO ML channel estimation ML estimate MLSD MMSE MMSE filtering MMSE-SC detector MMSE-SC equalizer multipath multiuser detection mutual information obtained OFDM optimal orthogonal output pilot symbols posteriori probability Pr(b recursion rewritten as follows sequence detection shown in Eq shown in Fig simulation results spreading codes subcarriers survival path symbol sequence time-varying channels transmitted trellis diagram users zero

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Page 318 - MS degree in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 1980, and the Ph.D.