Maximum Likelihood Decoding for Channels with Uniform Noise and Signal Dependent Offset

Abstract

Maximum likelihood (ML) decision criteria have been developed for channels suffering from signal independent offset mismatch. Here, such criteria are considered for signal dependent offset, which means that the value of the offset may differ for distinct signal levels rather than being the same for all levels. An ML decision criterion is derived, assuming uniform distributions for both the noise and the offset. In particular, for the proposed ML decoder, bounds are determined on the standard deviations of the noise and the offset which lead to a word error rate equal to zero. Simulation results are presented confirming the findings.