Joint data and channel estimation using the per-branch processing method

Joint data and channel estimation in a wireless communication system and overfrequency selective Rayleigh fading channels can be performed by implementing maximum likelihood sequence estimation (MLSE) using the per-survivor processing (PSP) method. However PSP can be used only if there is one channel estimation per symbol interval. In this paper we introduce the per-branch processing (PBP) method as a general case of PSP, which has the advantages of PSP and allows more than one estimation per symbol to improve the receiver error performance in fast fading. The Kalman filter is considered for channel estimation and the overall bit error rate (BER) performance is shown to be superior to that of detection techniques using the RLS and LMS estimators. Three different square-root methods for implementation of the Kalman filter are analyzed and compared with the RLS and LMS algorithms based on different number of bits required for implementation.