Adaptive MLSDE Receivers for Wireless Communications

This thesis presents the structural design, performance evaluation and complexity reduction of adaptive maximum likelihood sequence detection and estimation (MLSDE) receivers for wireless communications. The receiver structure is developed based on the maximum likelihood (ML) criterion for joint channel estimation and data detection and using the expectation and maximization (EM) algorithm. Generalized MLSDE (GMLSDE) algorithm, which alternates between estimation and detection and at the same time increases the likelihood iteratively, is developed using the on-line EM algorithm. The GMLSDE provides the theoretical ML base from which per-survivor processing (using different estimators for each survivor path) and conventional channel estimation (using only one estimator for all survivor paths) can be deduced for different channel models. Numerous adaptive MLSDE receiver structures are developed for different channel models from the GMLSDE algorithm using causal estimation and detection methods which only guarantee increasing the likelihood. Although some structures are new, th