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Sort-and-match algorithm for soft-decision decoding

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1 Author(s)
Dumer, I. ; Coll. of Eng., California Univ., Riverside, CA, USA

Let a q-ary linear (n, k) code C be used over a memoryless channel. We design a decoding algorithm ΨN that splits the received block into two halves in n different ways. First, about √N error patterns are found on either half. Then the left- and right-hand lists are sorted out and matched to form codewords. Finally, the most probable codeword is chosen among at most n√N codewords obtained in all n trials. The algorithm can be applied to any linear code C and has complexity order of n3√N. For any N⩾qn-k, the decoding error probability PN exceeds at most 1+qn-k/N times the probability PΨ (C) of maximum-likelihood decoding. For code rates R⩾1/2, the complexity order qn-k/2 grows as square root of general trellis complexity qmin{n-k,k}. When used on quantized additive white Gaussian noise (AWGN) channels, the algorithm ΨN can provide maximum-likelihood decoding for most binary linear codes even when N has an exponential order of qn-k

Published in:

Information Theory, IEEE Transactions on  (Volume:45 ,  Issue: 7 )

Date of Publication:

Nov 1999

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