基於低密度奇偶查核碼的分散式訊源編碼機制

A Study of Low-Density Parity Check Code for Distributed Source Coding

王韋超(Wei-Chao Wang)

指導教授 ： 張文輝

國立交通大學/電機學院/電信工程系所/碩士(2012年)

https://doi.org/10.6842/NCTU.2012.00299

摘要

本論文針對二位元相關訊源，利用低密度奇偶查核碼具體實現Slepian-Wolf理論在分散式訊源編碼的應用。訊源相關模型以兩種虛擬通道模型表示，分別是二位元對稱通道與Gilbert通道。針對訊源編碼輸出的校驗子經由雜訊通道傳輸的問題，我們提出基於渦輪碼原則推導的疊代訊源通道解碼。我們將考慮兩種通道碼，低密度奇偶查核碼應用在分散式訊源編碼的校驗子生成以達到資料壓縮效果，而迴旋碼則用於提昇壓縮資料對抗通道雜訊的能力。模擬結果顯示基於低密度奇偶查核碼的分散式訊源編碼機制，配合疊代訊源通道解碼演算法，可同時兼顧高壓縮率及強健性能。

關鍵字

低密度奇偶查核碼 ； 分散式訊源編碼 ； Gilbert 通道 ； 疊代訊源通道解碼

並列摘要

In this thesis, we study the use of low-density parity check (LDPC) codes for distributed source coding (DSC) of correlated binary sources. The Slepian-Wolf theorem states that there is no less in rate to compress two correlated sources using separate encoding, provided that the decoding is done jointly and the source correlation is available to both the encoder and decoder. Source correlation is modeled by two types of virtual channels: binary symmetric channel (BSC) and Gilbert channel. Also proposed is an iterative source-channel decoding (ISCD) algorithm for dealing with the Slepian-Wolf problem over noisy channel. An outer LDPC code is used to perform DSC, and an inner convolution code is used for enhancing the error protecting capability of the compressed data. Simulation results indicate the combined use of ISCD and LDPC-based DSC can provide error robustness as well as channel efficiency.

並列關鍵字

low-density parity check (LDPC) ； distributed source coding (DSC) ； iterative source-channel decoding (ISCD)

參考文獻

[3] G. A. Margulis, “Explicit Construction of Graphs without Short Cycles and Low Density Codes,” Combinatorica, vol. 2, no. 1, pp. 71–78, 1982.

[4] R. M. Tanner, “A Recursive Approach to Low Complexity Codes,” IEEE Trans. Inf.Theory,vol. IT-27, no. 5, pp. 533–547, Sep. 1981.

[5] M. Sipser and D. A. Spielman, “Expander codes,” IEEE Trans. Inform. Theory, vol. 42, pp. 1710–1722, Nov. 1996.

[6] D. J. C. MacKay and R. M. Neal, “Near Shannon Limit Performance of Low Density Parity Check Codes,” Electron. Lett., vol. 32, pp. 1645–1646, Aug. 1996.

[7] D. J. C. MacKay, “Good Error Correcting Codes Based on Very Sparse Matrices,” IEEE Trans. Inf. Theory, vol. 45, no. 2, pp. 399–431, Mar. 1999.