Computational complexity of sequential sequence estimation for intersymbol interference channels

The computational complexity of a sequential algorithm (SA) developed for intersymbol interference (ISI) channels is analyzed. To determine the computational complexity, the finite-state machine that models the channel and white matched filter system, of which the SA is a part, is interpreted as a special convolutional encoder followed by a binary symbol to Q-ary symbol mapping. It follows that the computational distribution is Pareto, and that there exists a computational cutoff rate R/sub comp/. For the uncoded data considered, the rate is fixed and the R/sub comp/ criterion translates into a signal-to-noise ratio (SNR) criterion. An upper bound on SNR/sub comp/ is found analytically by assuming a uniform input distribution. Iteration equations developed by S. Arimoto (1976) are adapted to find the true SNR/sub comp/ numerically.<
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