MAP Sequence Estimation for Fading ISI

| In this paper we rst describe a Maximum A Posterior (MAP) based sequence estimation approach for unknown, fast fading, frequency selective digital communications channels. The approach incorporates prior probabilistic knowledge of the channel via a stochastic channel model. We then assume a rst order Gauss-Markov channel model to derive a speciic MAP estimator, and we describe a Per Survivor Processing (PSP) algorithm as a realistic approximation of the optimum estimator. Monte Carlo simulations results are then presented: rst to illustrate the performance realized when incorporating prior channel knowledge and its dependence on channel fading rate; and second to study the sensitivity of this MAP estimator to inaccuracies in the assumed values of parameters of the Gauss-Markov model. I. Introduction MAP sequence estimation is widely used in digital communication systems to estimate the transmitted data sequence observed over noisy, multipath channels. This objective becomes particularly challenging when the channel is unknown and fast-fading. If the channel is slow time-varying, eeec-tive algorithms 1, 2, 3, 4] have been developed to estimate the transmitted data sequence and/or identify the channel blindly. For fast time-varying unknown channels, adaptive MLSE approaches have been developed. These algorithms usually estimate the transmitted data sequence based on the current estimate of the channel coeecients, while the channel coeecients are estimated using an estimate of the transmitted data sequence. One method of channel estimation 5] is to make tentative decisions which are obtained by truncating the surviving path in the Viterbi algorithm 6] to a xed delay. Another channel estimator is realized by an adaptive decision-feedback equalizer (DFE) embedded in the MLSE structure 7]. Due to the decision delay in the former method and the error propagation caused by the DFE in the latter, these two methods have diiculty in tracking fast time-varying intersym-bol interference (ISI) channels. In 8] we have developed a maximum a posterior (MAP) sequence estimator for unknown, fast fading ISI channels for which the channel is modeled as FIR with a rst-order Gauss-Markov coeecient vector. We derived a closed-form MAP cost function by marginalizing over the channel coeecients. For