Diversity with practical channel estimation in arbitrary fading environments

Abstract

This thesis presents a framework for evaluating the bit error probability of Nd-branch diversity combining in the presence of non-ideal channel estimates. The estimator structure is based on the maximum likelihood (ML) estimate and arises naturally as the sample mean of Np pilot symbols. The framework presented requires only the evaluation of a single integral involving the moment generating function of the norm square of the channel gain vector, and is applicable to channels with arbitrary distribution, including correlated fading. Analytical results show that the practical ML channel estimator preserves the diversity order of an Nd-branch diversity system, contrary to conclusions in the literature based upon a model that assumes a fixed correlation between the channel and its estimate. Finally, the asymptotic signal-to-noise ratio (SNR) penalty due to estimation error is investigated. This investigation reveals that the penalty has surprisingly little dependence on the number of diversity branches.