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姓名 張勝瑋(Sheng-Wei Chang) 查詢紙本館藏 畢業系所 通訊工程學系 論文名稱 LDPC碼之混合解碼法-SPA法與LPWBF演算法
(On the Hybrid Decoding Method for LDPC Code by Using the SPA and the LPWBF Algorithms)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) LDPC codes 在消息理論上越來越獲得重視,然而一個主要的問題就是面對這些碼在傳輸系統裡是高解碼複雜度導致需要較長的解碼時間,像這樣較長的解碼時間在許多應用上是不被接受的。利用訊息傳遞解碼演算法,我們發現在解碼方面有很好的效能,但因為其複雜度偏高所花的解碼時間也比較長,所以,很多研究都在討論如何改善複雜度,又不會使效能嚴重的衰減。相對於硬式解碼演算法在效能方面不像軟式解碼演算法這麼出色,不過,硬式解碼演算法也有優點,像是複雜度低、易於硬體實現且解碼時間較快。在這篇論文,主要是討論藉由混合硬式與軟式解碼演算法使其在效能與解法時間上取得一個平衡,讓效能可以維持一定的水準,且使用較少的運算量,減少解碼時間。 摘要(英) There are increased emphasis in Low-density parity check(LDPC) codes in information theory field. However, one of the main problems is that due to its high decoding complexity in communication system, which leads to much decoding time, it is not acceptable in many applications. We can find that it have good performance in decoding by using message passing algorithm. However, due to its high complexity, which leads to much decoding time, a lot research are studied and discussed how to improve its complexity, which may not decay its performance seriously. Comparing to soft decoding algorithms, hard decoding algorithms are not as outstanding as soft decoding algorithm in the aspect of performance; however there are some advantages such as low complexity, which can be realized in hardware easily and lower decoding time. In this thesis, I mainly discuss that by mixing soft-decision and hard-decision algorithms can we get a balance between performance and decoding time, which can keep its performance in certain level and use less operations to reduce decoding time. 關鍵字(中) ★ 混合解碼演算法
★ 低密度奇偶檢查碼關鍵字(英) ★ LDPC code
★ hybrid decoding algorithm論文目次 摘要I
ABSTRACTII
目錄IV
圖目錄VI
表目錄VIII
第一章 前言1
1.1 研究內容與論文動機1
1.2 論文組織1
第二章 LDPC CODE2
2.1線性區塊碼(LINEAR BLOCK CODE)2
2.1.1 線性區塊碼定義2
2.1.2 生成矩陣與奇偶檢驗矩陣2
2.1.3 漢明權數與漢明距離5
2.2 LDPC CODE的起源6
2.3 LDPC CODE之介紹6
2.3.1 LDPC Code之矩陣表示方法與定義6
2.3.2 Tanner graph7
2.4 LDPC CODE之設計架構類型8
2.4.1隨機構造方法9
2.4.1.1規則性(Regular) LDPC Codes9
Gallager 方法9
Mackay-1A方法9
2.4.1.2 不規則性(Irregular) LDPC Codes10
Mackay-2A方法10
2.5 LDPC CODE之編碼法10
第三章 解碼演算法12
3.1 MESSAGE PASSING軟式解碼演算法13
3.1.1 Sum-Product algorithm14
3.1.2 Min-Sum algorithm18
3.2低複雜度硬式解碼演算法19
3.2.1 Bit-Flipping(BF) algorithm 19
3.2.2 Weighted Bit-Flipping(WBF) algorithm20
3.2.3 Liu-Pados(LP) Weighted Bit-Flipping algorithm22
3.2.4 Reliability Ratio(RR) Weighted Bit-Flipping algorithm24
3.3 混合解碼演算法25
3.3.1動機25
3.3.2軟式與硬式解碼法的選擇25
3.3.3疊代次數的分配25
3.3.4解碼流程圖26
第四章 模擬結果與分析27
第五章 結論48
參考文獻49
附錄51參考文獻 [1] Gallager, R.G.: “Low density parity check codes”, IRE Trans. Inf. Theory, pp. 21–28. 1962, 8
[2] R. M. Tanner, “A recursive approach to low complexity codes,”IEEE Trans. Inform. Theory, Vol. 27, pp. 533-547, Sept. 1981.
[3] D. J. C. MacKay and R. M. Neal, “Near Shannon limit performance of low density parity check codes,” Electron. Lett., Vol 32, no. 18, pp. 1645-1646, Aug. 1996.
[4] N. Wiberg, “Codes and Decoding on General Graphs,” Ph.D. thesis, Linkoping University, Sweden, 1996.
[5] D. J. C. MacKay, “Gallager codes that are better than turbo codes,” in Proc. 36th Allerton Conf . Comm., Control, and Computing, Sept. 1998.
[6] T. Richardson, A. Shokrollahi and R. Urbanke, “Design of capacityapproaching irregular codes,” IEEE Trans. Inform. Theory, Vol. 47, pp. 619-637, Feb. 2001.
[7] T. Richardson and R. Urbanke, “The capacity of low density parity check codes under message-passing decoding,” IEEE Trans. Inform.Theory, Vol. 47, pp. 599-618, Feb. 2001.
[8] S.-Y. Chung, G. D. Forney, T. Richardson and R. Urbanke, “On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit,” IEEE Comm. Lett., Vol. 5, pp. 58-60, Feb. 2001.
[9] MacKay, D.J.C.: “Good error-correcting codes based on very sparse matrices”, IEEE Trans. Inf. Theory, 45, pp. 399–432, March 1999
[10] Y. Kou, S. Lin, and M. P. Fossorier, “Low-density parity-check codes based on finite geometries: a rediscovery and new results,” IEEE Trans. Inform. Theory, Vol. 47, no. 7, pp. 2711–2736, Nov. 2001.
[11] John L. Fan ,“Constrained coding and soft iterative decoding” Kluwer Academic Publishers, 2001.
[12] M. Miladinovic, M. P. C. Fossorier and H. Imai, “Reduced complexity iterative decoding of low-density parity check codes based on belief propagation,” IEEE Trans. Comm., Vol. 47, pp. 673-680, May 1999.
[13] Liu, Z., and Pados, D.A.: ‘Low complexity decoding of finite geometry LDPC codes’, Communications, Vol.4,pp. 2713–2717,May 2003
[14] F. Guo and L. Hanzo, “Reliability ratio based weighted bit-flipping decoding for low-density parity-check codes,” IEEE Electron. Lett., Vol. 40, pp. 1356-1358, Oct. 2004.
[15] M. Shan, C.M. Zhao and M. Jiang ,” Improved weighted bit-flipping algorithm for decoding LDPC codes,” IEE Pro.- Commun. , Vol. 152, No. 6, pp. 919-922, December 2005
[16] David J.C MacKay:Cavendish Laboratory, Cambridge. Get from http://www.inference.phy.cam.ac.uk/mackay/codes/data.html
[17] 林銀議, “數位通訊原理 編碼與消息理論,” 五南, 2005
[18] HR Zeidan, MM Elsabrouty , “Two-Stage Hybrid decoding for Low-Density Parity-Check codes”, Innovations in Information Technology, pp.650-654, Nov. 2007指導教授 賀嘉律(Chia-Lu Ho) 審核日期 2008-7-16 推文 plurk
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