Analysis of Joint Source Channel LDPC Coding for Correlated Sources Transmission over Noisy Channels

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.

Authors:

• Marwa Ben Abdessalem
• Amin Zribi
• Ammar Bouallègue

Keywords:

• AWGN channel
• belief propagation
• joint source channel coding
• LDPC codes.

References:

[1] C. E. Shannon, “A mathematical theory of communication,” Bell System
Technical Journal, vol. 27, pp. 379-423, 623–656, 1948.
[2] 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.
[3] T. M. Cover and J. A. Thomas, “Elements of Information Theory,” John
Wiley, New York, 1991.
[4] 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.
[5] 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.
[6] R. Bauer and J. Hagenauer, “On variable length codes for iterative
source/channel decoding,” Proc. Data Compression Conference, pp.
273–282, April. 2001.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.