METRIC SIFTING IN BREADTH-FIRST DECODING OF CONVOLUTIONAL CODED DATA

摘要

A metric sifting, i.e., sorting or selection, method which can efficiently provide a sorted set of survivor metrics during breadth-first reduced-search decoding of convolutional codes. The preferred embodiment efficiently implements the M algorithm by providing a sorted set of M survivor metrics using a linear-time (OM) number of comparisons. To obtain this efficieny, the method employs partitioning of branch metrics into implicitly sorted subsets, and employs efficient merging of these subsets. Compared to the prior art, the number of comparisons for metric sifting during M algorithm decoding of typical rate 1/n binary convolutional codes is reduced by 30-40 %; more specifically, the number of comparisons is reduced from (2n-1)M-(n-1) to n(M-1)+2n-1.