Adaptive Bayesian decision feedback equaliser incorporating co-channel interference compensation

The paper derives a Bayesian decision feedback equaliser (DFE) which incorporates co-channel interference (CCI) compensation. By exploiting the structure of CCI signals, the proposed Bayesian DFE can distinguish an interfering signal from white noise and uses this information to improve performance. Adaptive implementation of this Bayesian DFE includes first using the standard least mean square (LMS) algorithm to identify the channel model and then estimating the co-channel states by means of a simple unsupervised clustering algorithm. Simulation involving a binary signal constellation is used to compare both the theoretical and adaptive performance of this Bayesian DFE with those of the maximum likelihood sequence estimator (MLSE). The results obtained indicate that, by compensating CCI the Bayesian DFE can outperform the MLSE for the CCI