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Efficient source decoding over memoryless noisy channels using higher order Markov models

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2 Author(s)
F. Lahouti ; Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Ont., Canada ; A. K. Khandani

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a γ-order Markov model (γ≥1) and a delay of δ,δ>0, is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms.

Published in:

IEEE Transactions on Information Theory  (Volume:50 ,  Issue: 9 )