Parallel structures for joint channel estimation and data detection over fading channels

Abstract

New parallel structures are proposed for joint data and channel estimation over frequency selective Rayleigh fading channels. Maximum likelihood sequence estimation (MLSE) is implemented using the per-survivor processing (PSP) method. The Kalman filter and the recursive least squares (RLS) algorithm are considered as estimation methods. A square-root implementation of the Kalman filter is discussed. The algorithm used for the measurement update in the Kalman filter results in significant simplicity, once it is used for realization of the RLS algorithm. Two parallel and pipelined architectures are introduced for the RLS algorithm, and an overall architecture is proposed to implement the MLSE receiver, combining the Viterbi decoder and the channel estimator.