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_comp. For the uncoded data considered, the rate is fixed and the R_comp criterion translates into a signal-to-noise ratio (SNR) criterion. An upper bound on SNR_comp is found analytically by assuming a uniform input distribution. Iteration equations developed by S. Arimoto (1976) are adapted to find the true SNR_comp numerically