Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/97041
標題: 低密度查核碼最小和解碼演算法之改良
A Modified Min-Sum Decoding Algorithm for LDPC Codes
作者: 陳俊瑋
Jun-Wei Chen
關鍵字: 低密度查核碼
錯誤更正碼
分解圖形
LDPC Code
Error control coding
factor graph
引用: [1] R. G. Gallager, “Low-Density Parity-Check Codes,” IRE Trans. Inform.Theroy, pp. 21-28, Jan. 1962. [2] D. MacKay, R. Neal, “Good codes based on very sparse matrices,” in Proc.5th IMA Conf. Cryptography and Coding, C. Boyd, Ed., Lecture Notes in Computer Science, pp. 100-111, Berlin, Germany: Springer, 1995. [3] R. G. Gallager, Low-Density Parity-Check Codes, Cambridge, MA: MIT Press,1963. [4] T. Richardson, A. Shokrollahi and R. Urbanke, “Design of capacity approachingirregular codes,” IEEE Trans. Inform. Theory, vol. 47, pp. 619-637, Feb. 2001. [5] T. J. Richardson and R. L. Urbanke, “Efficient encoding of low-density parity-check codes,” IEEE Trans. Inform. Theory, vol. 47, pp. 638-656, Feb.2001. [6] D. J. C. MacKay, “Gallager codes that are better than turbo codes,” in Proc. 36th Allerton Conf. Comm., Control, and Computing, Sept. 1998. [7] IEEE Std 802.16e-2005, 2006. Standard for Local and metropolitan area networks Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems Amendment 2: Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands and Corrigendum 1,IEEE, New York, USA. [8] H. Zhong and T. Zhang, “Block-LDPC: A practical LDPC coding system design approach,” IEEE Tran. TCSI, vol. 52, pp. 766-775, Apr. 2005. [9] T. Richardson, A. Shokrollahi and R. Urbanke, “Design of capacity approaching rregular codes,” IEEE Trans. Inform. Theory, vol. 47, pp. 619-637, Feb. 2001. [10] M. Fossorier, M. Mihaljevic, H.Imai, “Reduced Complexity Iterative Decoding of Low-Density Parity Check Codes Based on Belief Propagation,” IEEE Trans. On Commun, vol. 47, no. 5, pp. 673-680, May 1999. [11] TurboBest,“IEEE 802.16e LDPC Encoder/Decoder Core”,TechnicalWhitePaper,http://www.turbobest.com/WhitePaper80216eLDPC.pdf
摘要: LDPC碼的解碼方法一般常見的方法有兩種,一般常見的演算法為和積演算法,但其複雜度高。而最小和演算法是一種最常用來簡化和積演算法的複雜度,但是效能比和積演算法低。為了解決這個問題,本論文探討對最小和演算法做補償技術,我提出的研究就是把最小和演算法中的平行解碼(check to bit)乘上縮放因子,希望藉由補償技術將最小和演算法修正,使其達到與傳統的和積演算法一樣好的效能。 我們以 IEEE 802.16e 系統做模擬實驗,模擬結果顯示,經過補償後的modified min-sum decoding,不但保有硬體簡化的特性,其解碼錯誤更正效能也十分接近傳統的和積演算法。
The LDPC code decoding methods generally are two common methods. The sum-product algorithm is usually used in LDPC codes, but with high complexity. The min-sum algorithm is usually used to reduce the complexity of the sum-product algorithm, but with lower performance than the sum-product algorithm. In this thesis, to solve this Problem, This paper explores a compensation techniques for the min-sum algorithm, I propose research even the min-sum algorithm of check to bit multiplication a Scaling factor, expect by compensation techniques for the min-sum algorithm make corrections, reach with traditional the same good performance for sum-product algorithm. We use the IEEE 802.16e system to do the simulation experiment, the simulation results show that the modified modified min-sum decoding, not only retain the hardware simplification of the characteristics of its decoding error correction performance is also very close to the traditional and sum-product algorithm.
URI: http://hdl.handle.net/11455/97041
文章公開時間: 2020-08-04
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