Block adaptive techniques for channel identification and data demodulation over band-limited channels

A new approach to the problem of data detection for communications over band-limited channels with unknown parameters is introduced. We propose a new way to implement the Viterbi algorithm (VA) for maximum-likelihood data sequence estimation (MLSE) in a known channel environment and utilize it to derive block adaptive techniques for joint channel and data estimation, when the channel-impulse response (CIR) is unknown. We show, via simulations, that we can achieve a probability of error very close to that of the known channel environment and nearly reach a mean-square error in the channel estimate as predicted by analytical bounds, operating on static channels, which exhibit deep nulls in their magnitude response and nonlinear phase. The proposed schemes accomplish channel acquisition after processing a few hundred symbols while operating without a training sequence, whereas linear blind equalizers, such as Sato's (1975) algorithm, fail to converge at all. The application of block processing to adaptive MLSE is also investigated for time-varying frequency-selective Rayleigh-fading channels, which are used for modeling mobile communication systems. In such environments it is shown that the proposed scheme exhibits improved performance compared to the conventional adaptive MLSE receiver using tentative delayed decisions.