Equalization and Interference Cancelation

  • Gordon L. Stüber


This chapter concentrates on the modeling of intersymbol interference (ISI) channels and the various signal processing methods for recovering digital information transmitted over such channels. The chapter begins with a treatment of ISI channel modeling, including a vector representation of digital signaling on ISI channels. We then develop the maximum likelihood receiver for ISI channels, leading to an equivalent model of the ISI channel known as the discrete-time white noise channel model. We also consider the effects of using fractional sampling or over-sampling at the receiver, where the sampling rate is an integer multiple of the modulated symbol rate. We then consider the various time-domain equalization techniques, including the linear zero-forcing and minimum mean-square-error equalizers, and the nonlinear decision feedback equalizer. Later, we consider sequence estimators beginning with maximum likelihood sequence estimation (MLSE) and the Viterbi algorithm. Since the MLSE receiver can have high complexity for channels that have a long impulse response, we consider some reduced complexity sequence estimation techniques such as reduced state sequence estimation (RSSE) and delayed decision feedback sequence estimation (DDFSE). The chapter goes on to provide an analysis of the bit error rate performance of MLSE on static ISI channels and multipath fading ISI channels, and fractionally spaced MLSE receivers for ISI channels. Finally, the chapter provides a discussion of co-channel demodulation for digital signals on ISI channels, and concludes with a receiver structure that incorporates a combination of optimal combining and sequence estimation as implemented with the Viterbi algorithm.


Additive White Gaussian Noise Interference Cancelation Channel Impulse Response Viterbi Algorithm Channel Vector 
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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Georgia Institute of TechnologyAtlantaUSA

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