Bayesian ML Sequence Detection for ISI Channels
Jill K. Nelson, Andrew C. Singer · 2006
We propose a Bayesian technique for blind detection of coded data transmitted over a dispersive channel. The Bayesian maximum likelihood sequence detector views the channel taps as stochastic quantities drawn from a known distribution and computes the probability of any transmitted sequence by averaging over the tap values. The resulting path metric requires memory of all previous symbols, and hence a tree-based algorithm is employed to find the most likely transmitted sequence. Simulation results show that the Bayesian detector can achieve bit error rates within 1/4 dB of the conventional known-channel maximum likelihood (ML) sequence detector.