Bayesian decision feedback techniques for deconvolution

This paper examines reduced complexity symbol-by-symbol demodulation in the presence of ISI. A new algorithm is derived by simplifying the MAP estimator using conditional decision feedback. The resulting family of Bayesian conditional decision feedback estimators (BCDFE) are computationally and performance competitive with the maximum likelihood sequence estimation. The BCDFEs are indexed by two parameters: a “chip” length and an estimation lag. These algorithms can be used with estimation lags greater than the equivalent channel length, and have a complexity which is exponential in the chip length but only linear in the estimation lag. In the unknown channel case recursive channel estimation is combined with the BCDFE to produce a high performance equalizer. Extensive simulations characterize the performance of the BCDFE for uncoded linear modulation over both known and unknown channels