Adaptively optimized decision feedback equalization for convolutional coding

A modified decision feedback equalizer (MDFE) which can compensate for error propagation was derived by Jung-Tao Lui and Gelfand (see Thirty-Six Annual Allerton Conference on Communication, Control, and Computing, 1998). The key property of the MDFE is its ability to shorten burst errors due to error propagation, and hence obtain an improved BER performance compared with the conventional DFE in a coded system. Here, we derive an LMS-type adaptive MDFE solution which incorporates the error propagation model into the training. The LMS MDFE is compared with the (offline) DFE and MDFE, and also two other adaptive DFE solutions found using an LMS algorithm with training data. Although slightly more complex than the other algorithms, the simulations suggest that the LMS MDFE has the best overall performance in a convolutionally coded system