| 总结: | To overcome the disadvantage of Viterbi decoding algorithm, in which its complexity exponentially increases with the increasing constraint length of convolutional codes, and the decoding delay was too large to fit the decoding of longer constraint length convolutional codes, a fast decoding of convolutional codes for longer constraint length, based on improved particle swarm optimization algorithm, was proposed. The proposed method reduces the searching area in the grid of decoding and shortens the decoding delay by setting the population size M to determine the number of decoding path, therefore was more suitable for longer constraint length convolutional codes. Another method of decoding convolu- tional codes based on self-adapting of decoding width was also proposed. Simulation results show that the proposed both methods have advantages in reducing the computational complexity and the decoding time.
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