Commenced in January 2007
Paper Count: 30998
Analysis of Joint Source Channel LDPC Coding for Correlated Sources Transmission over Noisy Channels
Abstract:In this paper, a Joint Source Channel coding scheme based on LDPC codes is investigated. We consider two concatenated LDPC codes, one allows to compress a correlated source and the second to protect it against channel degradations. The original information can be reconstructed at the receiver by a joint decoder, where the source decoder and the channel decoder run in parallel by transferring extrinsic information. We investigate the performance of the JSC LDPC code in terms of Bit-Error Rate (BER) in the case of transmission over an Additive White Gaussian Noise (AWGN) channel, and for different source and channel rate parameters. We emphasize how JSC LDPC presents a performance tradeoff depending on the channel state and on the source correlation. We show that, the JSC LDPC is an efficient solution for a relatively low Signal-to-Noise Ratio (SNR) channel, especially with highly correlated sources. Finally, a source-channel rate optimization has to be applied to guarantee the best JSC LDPC system performance for a given channel.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340122Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 571
 C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, pp. 379-423, 623–656, 1948.
 S. Vembu, S. Verdu, and Y. Steinberg, “The source-channel separation theorem revisited,” IEEE Trans. on Inform. Theory, vol. 41, no. 1, pp. 44–54, Jan. 1995.
 T. M. Cover and J. A. Thomas, “Elements of Information Theory,” John Wiley, New York, 1991.
 K. Sayood and J. C. Borkenhagen, “Use of residual redundancy in the design of joint source/channel coders,” IEEE Trans. on Comm., vol. 39, no. 6, pp. 838–846, June 1991.
 A. Zribi, R. Pyndiah, S. Zaibi, F. Guilloud, and A. Bouallgue, “Low-complexity soft decoding of Huffman codes and iterative joint source/channel decoding,” IEEE Trans. on comm., vol. 60, n. 6, pp. 1669–1679, Jun. 2012.
 R. Bauer and J. Hagenauer, “On variable length codes for iterative source/channel decoding,” Proc. Data Compression Conference, pp. 273–282, April. 2001.
 J. Kliewer and R. Thobaben, “Iterative joint source–channel decoding of variable length codes using residual source redundancy,” IEEE Trans. Wireless. Commun., vol. 4, no. 3, pp. 919–929, May 2005.
 H. Nguyen and P. Duhamel, “Robust source decoding of varaible–length encoded video data taking into account source constraints ,” IEEE Trans. Commun., vol. 53, no. 7, pp. 1077–1084, Jul. 2005.
 M. Fresia, F. Prez-Cruz, H. V. Poor, and S. Verdu, “Joint source and channel coding,” IEEE Sig. Proc. Mag., vol.27, pp. 104–113, Nov. 2010.
 R. Asvadi, T. Matsumoto, and M. J. Juntti, “Optimized LDPC codes for joint source-channel decoding of quantized Gauss-Markov signals,” IEEE International Conference on Communications (ICC), pp. 5233–5238, 2014.
 T. Hindelang, J. Hagenauer, and S. Heinen, “Sources–controlled channel decoding: Estimation of correlated parameters,” in Proc. 3rd Int. ITG Conf. on source and channel coding, pp. 259–266, Munich, Germany, 2000.
 M. A. Mohd Izhar, N. Fisal, X. Zhou, K. Anwar, and T. Matsumoto, “Exploitation of 2D binary source correlation using turbo block codes with fine-tuning ,” EURASIP Journal on Wireless Communications and Networking, vol. 2013, no. 1, pp. 1–11, 2013.