Perspectives of Monge properties in optimization
Discrete Applied Mathematics
Pattern matching algorithms
Joint Source/Channel Coding for Variable Length Codes
DCC '98 Proceedings of the Conference on Data Compression
Detection of binary Markov sources over channels with additive Markov noise
IEEE Transactions on Information Theory
Monotonicity-based fast algorithms for MAP estimation of Markov sequences over noisy channels
IEEE Transactions on Information Theory
Joint turbo decoding and estimation of hidden Markov sources
IEEE Journal on Selected Areas in Communications
Joint source-channel turbo decoding of entropy-coded sources
IEEE Journal on Selected Areas in Communications
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This work addresses the problem of joint source-channel decoding of a Markov sequence which is first encoded by a source code, then encoded by a convolutional code, and sent through a noisy memoryless channel. It is shown that for Markov sources satisfying the so-called Monge property, both the maximum a posteriori probability (MAP) sequence decoding, as well as the soft output Max-Log-MAP decoding can be accelerated by a factor of K without compromising the optimality, where K is the size of the Markov source alphabet. The key to achieve a higher decoding speed is a convenient organization of computations at the decoder combined with a fast matrix search technique enabled by the Monge property. The same decrease in complexity follows, as a by-product of the development, for the soft output Max-Log-MAP joint source channel decoding in the case when the convolutional coder is absent, result which was not known previously.