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In this paper, we propose a parallel block-based Viterbi decoder (PBVD) onthe graphic processing unit (GPU) platform for the decoding of convolutionalcodes. The decoding procedure is simplified and parallelized, and thecharacteristic of the trellis is exploited to reduce the metric computation.Based on the compute unified device architecture (CUDA), two kernels withdifferent parallelism are designed to map two decoding phases. Moreover, theoptimal design of data structures for several kinds of intermediate informationare presented, to improve the efficiency of internal memory transactions.Experimental results demonstrate that the proposed decoder achieves highthroughput of 598Mbps on NVIDIA GTX580 and 1802Mbps on GTX980 for the 64-stateconvolutional code, which are 1.5 times speedup compared to the existingfastest works on GPUs.

A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on GPU