Institute for Systems Research

We investigate the problem of detecting a binary iid non-uniform source transmitted across the Markov channel. Two maximum a posteriori (MAP) formulations are considered: a sequence MAP detection and an instantaneous MAP detection. The two MAP detection problems are implemented using a modified version of the Viterbi decoding algorithm and a recursive algorithm. Necessary and sufficient conditions under which the sequence MAP detector becomes useless as well as simulation results are presented. A comparison between the performance of the proposed system with that of a (substantially more complex) traditional tandem source-channel coding scheme exhibits a better performance for the proposed scheme at relatively high channel bit error rates.

The same detection problem is then analyzed for the case of a binary symmetric Markov source. Analytical and simulation results show the existence of a "mismatch" between the source and the channel. This mismatch is reduced by the use of a rate-one convolutional encoder. Finally, the detection problem is generalized for the case of a binary non-symmetric Markov source.

We First derive a maximum likelihood (ML) decoding algorithm for the channel; it turns out that ML decoding is equivalent to decoding a received vector onto either the closest codeword or the codeword that is farthest away, depending on whether an ﲡpparent epidemic has occurred. We next show that the Poly-contagion channel is an ﲡveraged channel in the sense of Ahlswede (and others) and that its capacity is zero. We then demonstrate that the Poly- contagion channel is a counter-example to the adage, ﲭemory cannot decrease capacity ; the capacity of the Poly-contagion channel is actually less than that of the associated memoryless channel. Finally, we consider a finite-memory version of the Poly-contagion model; this channel is (unlike the original) ergodic with a non-zero capacity that increases with increasing memory.