Fast Decoding of Convolutional Codes Based on Particle Swarm Optimization

The complexity of Viterbi decoding algorithm will increase exponentially to the constraint length of convolutional codes by index and the decoding delay is too large. So it only adapts to the decoding of shorter constraint length convolutional codes. Aiming at these shortcomings, this paper presents fast decoding of convolutional codes, which are based on particle swarm optimization (PSO) algorithm. The algorithm decides the number of decoding paths by setting up the population size M. So it could reduce the searching area in the trellis of decoding and shorten the decoding delay, thereby more adapts to longer constraint length convolutional codes. The simulation results show that the proposed algorithm reduce the bit error rate (BER) and the decoding time.