Fast Joint Source-Channel Decoding of Convolutional Coded Markov Sequences with Monge Property

作者：Dumitrescu Sorina^*

来源：IEEE Transactions on Communications, 2010, 58(1): 128-135.

DOI：10.1109/TCOMM.2010.01.080091

• Joint source-channel decoding
• maximum a posteriori probability sequence estimation
• Max-Log-MAP algorithm
• Markov sequence
• Monge property

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.

• 出版日期2010-1