@inproceedings{bb3688bba6d14b6dac4c0d7c47cee1cd,
title = "Bayesian ML sequence detection for ISI channels",
abstract = "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.",
author = "Nelson, \{Jill K.\} and Singer, \{Andrew C.\}",
year = "2006",
doi = "10.1109/CISS.2006.286556",
language = "English",
isbn = "1424403502",
series = "2006 IEEE Conference on Information Sciences and Systems, CISS 2006 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "693--698",
booktitle = "2006 IEEE Conference on Information Sciences and Systems, CISS 2006 - Proceedings",
note = "2006 40th Annual Conference on Information Sciences and Systems, CISS 2006 ; Conference date: 22-03-2006 Through 24-03-2006",
}