The Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is an algorithm for maximum a posteriori decoding of error correcting codes defined on trellises (principally convolutional codes). The algorithm is named after its inventors: Bahl, Cocke, Jelinek and Raviv.[1] This algorithm is critical to modern iteratively-decoded error-correcting codes, including turbo codes and low-density parity-check codes.
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Mod-01 Lec-34 BCJR Decoder
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Mod-01 Lec-35 BCJR and Max-Log-MAP Decoder, Introduction to Turbo Codes
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Lec 20 | MIT 6.451 Principles of Digital Communication II, Spring 2005
Transcription
Steps involved
Based on the trellis:
- Compute forward probabilities
- Compute backward probabilities
- Compute smoothed probabilities based on other information (i.e. noise variance for AWGN, bit crossover probability for binary symmetric channel)
Variations
SBGT BCJR
Berrou, Glavieux and Thitimajshima simplification.[2]
Log-Map BCJR
Implementations
- Susa framework implements BCJR algorithm for forward error correction codes and channel equalization in C++.
See also
References
- ^ Bahl, L.; Cocke, J.; Jelinek, F.; Raviv, J. (March 1974). "Optimal Decoding of Linear Codes for minimizing symbol error rate". IEEE Transactions on Information Theory. 20 (2): 284–7. doi:10.1109/TIT.1974.1055186.
- ^ Wang, Sichun; Patenaude, François (2006). "A Systematic Approach to Modified BCJR MAP Algorithms for Convolutional Codes". EURASIP Journal on Applied Signal Processing. 2006: 95360. Bibcode:2006EJASP2006..242W. doi:10.1155/ASP/2006/95360.
- ^ Robertson, P.; Hoeher, P.; Villebrun, E. (1997). "Optimal and Sub-Optimal Maximum A Posteriori Algorithms Suitable for Turbo Decoding". European Transactions on Telecommunications. 8 (2): 119–125. doi:10.1002/ett.4460080202.
External links
- The online textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay, discusses the BCJR algorithm in chapter 25.
- The implementation of BCJR algorithm in Susa signal processing framework
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This page was last edited on 21 June 2024, at 21:11