Optimal detection of M-QAM signal with channel estimation error

In this paper, we study the optimal detection of QAM signaling with channel estimation error. Optimal detectors, Maximum Likelihood (MaxLike) detector and a Modified Minimum Distance (ModDist) detector, are derived for the case of a known Gaussian channel distribution and unknown channel distribution, respectively. These detectors differ from the traditional minimum distance detector which ignores the effect of channel estimation error. Soft decision metrics for coded performance are also generated using the soft output of these detectors based on Generalized Likelihood Ratio (GLR). Numerical results are given to compare the performance of these detectors. Although only marginal improvement is observed when using the proposed detectors without coding, significant gains are obtained when used together with coding.