Presents sequence estimation for the frequency selective Rayleigh fading channel. Maximum likelihood estimation using the Viterbi algorithm (MLSE-VA) and sequential sequence estimation (SSE) are developed. Both MLSE-VA and SSE consist of a set of Kalman filters which estimate the fading channel as time evolves. Computer simulations for two different channel models show that the error performance of the two approaches is essentially the same. SSE however has considerably less computational complexity than MLSE-VA. To improve the error performance, diversity is combined with both MLSE-VA and SSE. The simulations show that diversity results in a signal to noise ratio gain of greater than 10 dB.>

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.>

Communication systems such as mobile radio and indoor wireless communication typically are modelled as frequency selective fading channels. This paper develops a sequential sequence estimator (SSE), which is essentially maximum likelihood, of the bandlimited signals with a convolutional coded sequence. Simulations show that the SSE with convolutional coded input sequence improves the performance significantly compared with that of SSE with diversity.>