Antenna subset diversity with non-ideal channel estimation

In modern wireless systems employing diversity techniques, combining all the available diversity branches may not be feasible due to complexity and resource constraints. To alleviate these issues, subset diversity (SSD) systems have been proposed. Here, we develop a framework for evaluating the symbol error probability for antenna SSD, where the signals from a subset of antenna elements are selected and combined in the presence of channel estimation error. We consider independent identically distributed Rayleigh fading channels and use an estimator structure based on the maximum likelihood (ML) estimate which arises naturally as the sample mean of N p pilot symbols. The analysis is valid for arbitrary two-dimensional signaling constellations. The expressions give insight into the performance losses of non-ideal SSD when compared to ideal SSD. Due to estimation error, these losses occur in branch combining as well as in branch selection. However, our analytical results show that the practical ML channel estimator still preserves the diversity order of an ideal SSD system with N d branches. Finally, we investigate the asymptotic signal-to-noise ratio penalty due to estimation error.