Adaptive MLSD receiver with identification of flat fading channels

This paper develops an adaptive maximum likelihood sequence detection (MLSD) algorithm for the Raleigh flat fading environment in association with channel coefficient estimation and channel identification. The design of the MLSD receiver depends on a knowledge of the channel. Along with different channel knowledge assumptions we consider the general case when the channel coefficient is time-variant and the channel statistical characteristics are unknown. The proposed adaptive algorithm has three recursive steps. The channel coefficient is estimated for each path in the trellis diagram by using Kalman filtering; then, based on a dynamic programming algorithm, the transmitted data is detected for each survivor path and, at the final step, the channel is identified by estimating the channel parameters associated with the best previous survivor path. The algorithm is able to track the changes in the channel parameters when the fading rate is changing due to the varying vehicle speed. Performance evaluation and comparisons are considered for different levels of channel knowledge by computer simulation