scholarly journals An Efficient Decoding Algorithm for Block Codes Based on the Communication Channel Reliability Information

2020 ◽  
Vol 4 (5) ◽  
pp. 336-344
Author(s):  
Yong-Geol Shim

For channel codes in communication systems, an efficient algorithm that controls error is proposed. It is an algorithm for soft decision decoding of block codes. The sufficient conditions to obtain the optimum decoding are deduced so that the efficient method which explores candidate code words can be presented. The information vector of signal space codes has isomorphic coherence. The path metric in the coded demodulator is the selected components of scaled regions. The carrier decision is derived by the normalized metric of synchronized space. An efficient algorithm is proposed based on the method. The algorithm finds out a group of candidate code words, in which the most likely one is chosen as a decoding result. The algorithm reduces the complexity, which is the number of candidate code words. It also increases the probability that the correct code word is included in the candidate code words. It is shown that both the error probability and the complexity are reduced. The positions of the first hard-decision decoded errors and the positions of the unreliable bits are carefully examined. From this examination, the candidate codewords are efficiently searched for. The aim of this paper is to reduce the required number of hard-decision decoding and to lower the block error probability.

1994 ◽  
Vol 2 (2) ◽  
pp. 145-164 ◽  
Author(s):  
Harpal Maini ◽  
Kishan Mehrotra ◽  
Chilukuri Mohan ◽  
Sanjay Ranka

Soft-decision decoding is an NP-hard problem of great interest to developers of communication systems. We show that this problem is equivalent to the problem of optimizing Walsh polynomials. We present genetic algorithms for soft-decision decoding of binary linear block codes and compare the performance with various other decoding algorithms including the currently developed A* algorithm. Simulation results show that our algorithms achieve bit-error-probabilities as low as 0.00183 for a [104,52] code with a low signal-to-noise ratio of 2.5 dB, exploring only 22,400 codewords, whereas the search space contains 4.5 × 10l5 codewords. We define a new crossover operator that exploits domain-specific information and compare it with uniform and two-point crossover.


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