6th International ICST Conference on Communications and Networking in China

Research Article

Serially Concatenated Joint Source-Channel Coding for Binary Markov Sources

  • @INPROCEEDINGS{10.1109/ChinaCom.2011.6158119,
        author={tadashi matsumoto and Xiaobo Zhou and Khoirul Anwar},
        title={Serially Concatenated Joint Source-Channel Coding for Binary Markov Sources},
        proceedings={6th International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2012},
        month={3},
        keywords={joint source channel coding exit chart binary markov source bcjr algorithm},
        doi={10.1109/ChinaCom.2011.6158119}
    }
    
  • tadashi matsumoto
    Xiaobo Zhou
    Khoirul Anwar
    Year: 2012
    Serially Concatenated Joint Source-Channel Coding for Binary Markov Sources
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2011.6158119
tadashi matsumoto,*, Xiaobo Zhou1, Khoirul Anwar1
  • 1: School of Information Science, Japan Advanced Institute of Science and Technology
*Contact email: matumoto@jaist.ac.jp

Abstract

In this paper, we propose a joint design of serially concatenated source channel coding for binary Markov sources over AWGN channels. To exploit the memory structure inherent within the sequence output from the source, modifications are made on the BCJR algorithm. To decode the outer code, the modified version of the BCJR algorithm is used, while the inner code by the standard version of the algorithm. Since optimal design of serially concatenated convolutional code falls into the problem of curve matching between the extrinsic information transfer (EXIT) curves of the inner and outer codes, we first evaluate the EXIT curve of the outer code decoded by the modified BCJR algorithm. It is then shown that the EXIT curve obtained by the modified BCJR algorithm is better matched with short memory inner convolutional code, which significantly re- duces coding/decoding complexity. Numerical results demonstrate significant gains over the systems in which source statistics are not exploited, and thereby narrowing the performance gap to the Shannon limit. We also compare in this paper the performance of the proposed design with the algorithm presented in F. Alajaji et. al. [1], designed also for transmission of binary Markov source using parallel concatenated convolutional code ([1] refers the technique as Joint Source Channel Turbo Code (JSCTC)). It is shown that our proposed system is superior in both system complexity and BER performance to the JSCTC technique presented in [1].