BCJR algorithm

The BCJR algorithm , the name is derived from the initials of the developers L. Bahl, J. Cocke , F. Jelinek and J. Raviv, was developed in 1974 for decoding block and convolutional codes . It is the optimal decoding algorithm (maximum a posteriori probability, MAP) in terms of the minimum symbol error probability and is used in particular for the iterative decoding of parallel or serially concatenated convolutional or block codes such as turbo codes .

The advantage of the BCJR algorithm for decoding convolutional codes by means of so-called soft decision consists in the efficient use of the information about the combination probabilities of successive code symbols, also referred to as Markov chains . Like the Viterbi algorithm , it can be displayed graphically in the form of a trellis diagram .

Individual evidence

↑ L. Bahl, J. Cocke, F. Jelinek, and J. Raviv: Optimal Decoding of Linear Codes for minimizing symbol error rate , published in IEEE Transactions on Information Theory, Issue IT-20 (2), pages 284 to 287, March 1974.

Web links

Online textbook: Information Theory, Inference, and Learning Algorithms , by David JC MacKay ; Chapter 25

[1] L. Bahl, J. Cocke, F. Jelinek, and J. Raviv: Optimal Decoding of Linear Codes for minimizing symbol error rate , published in IEEE Transactions on Information Theory, Issue IT-20 (2), pages 284 to 287, March 1974.