On blind channel estimation with periodic misses and equalization of periodically varying channels

Time series with systematic misses and signals passing through rapidly fading channels can be modeled as cyclostationary output sequences of linear periodically time-varying (LPTV) systems. Blind identification of finite parameter LPTV systems can be accomplished using cyclic second- and higher-order statistics of the cyclostationary output, even if the latter is corrupted by stationary noise of unknown spectral characteristics. Novel closed-form solutions for the modulating sequence which models periodic misses and the TV moving average parameters of periodic channels are developed. Using the estimated LPTV channel, the Viterbi algorithm, or a computationally efficient LPTV mean-squared error equalizer can be adopted to recover the information bearing input