一种基于遗传算法和神经网络的硬判决译码方案

A Hard Decision Decoding Scheme Based on the Genetic Algorithm and Neural Network

基于遗传算法和神经网络混合智能算法,提出一种新的硬判决译码方案,即遗传神经网络译码(genetic neural-network decoding,GND)。GND译码充分利用遗传算法的自优化能力和神经网络的模式分类功能,对接收匹配滤波器的硬判决量化输出进行优化处理,以弥补因信道传输误差和硬判决量化造成的译码的可靠性损失,恢复出与传输序列更似然的码字作为硬判决译码器的输入,从而得到更好的译码结果。从理论分析和计算机模拟仿真结果可看出:GND译码方案纠错性能接近传统软判决译码,但由于译码过程不需要利用信道统计软信息,其复杂度相对传统软判决译码大幅度降低。

Based on the hybrid intelligent algorithm of the genetic algorithm and neural network, a novel hard decision decoding scheme that is named as the Genetic Neural-network Decoding(GND) algorithm is proposed. GND decoding scheme offsets the reliability loss caused by channel transmission error and hard decision quantization by making full use of the genetic algorithm’s optimization capacity and neural network’s pattern classification function to optimize the hard decision outputs of received matched filter and restore a more likelihood codeword as the input of hard decision decoder. It can be known from the theoretical analysis and the computer simulation result that GND scheme is approximate to the traditional soft decision decoding in the error-correction performance. Furthermore, compared with the complexity of the traditional soft decision decoding scheme, the complexity of the GND scheme is greatly reduced because its decoding process does not need to utilize the channel statistical information.