Maximum-likelihood synchronization, equalization, and sequence estimation for unknown time-varying frequency-selective Rician channels

This paper develops a receiver structure to perform jointly maximum-likelihood (ML) synchronization, equalization, and detection of a linearly modulated signal transmitted over a time-varying frequency-selective Rician-faded channel, corrupted by additive white Gaussian noise (AWGN). The receiver is particularly suited to a fast-fading channel, where other receivers that rely on estimating the channel cannot track it quickly enough. The signal mean and autocovariance are needed, and a scheme is proposed for estimating these quantities adaptively. The receiver processes the specular and diffuse components (corresponding to the signal mean and autocovariance) separately. Processing the known specular component is the classical detection problem. The unknown diffuse component is processed by predictors [11]. We show that the predictors can achieve synchronization in a novel manner, if synchronization is required. A union bound on the receiver's bit-error rate (BER) is derived, and it tightly bounds simulated BER's in fast fading at high signal-to-noise ratios (SNR's). Index Terms - Dispersive channels, error analysis, linear prediction, maximum-likelihood detection, parameter estimation, Rician channels, synchronization, time-varying channels.