一种用于FEC译码技术的改进遗传算法  

An Improved Genetic Algorithm Applied in FEC Decoding Technologies

作　　者：袁建国[1] 田杨[1] 胡夏[1] 张杰[1] 舒梁博 

机构地区：[1]重庆邮电大学光纤通信技术重点实验室,重庆400065

出　　处：《半导体光电》2015年第1期103-105,共3页Semiconductor Optoelectronics

基　　金：国家自然科学基金项目(61071117;61275077;61003256);重庆市自然科学基金项目(2010BB2409);2013年重庆邮电大学大学生科研训练计划项目(A2013-42)

摘　　要：针对基本遗传算法中的早熟问题和因种内竞争而带来的"封闭竞争"问题,提出了一种改进的遗传算法。该算法增加了群体多样性,引入了相似度概念,并应用于种间竞争。其收敛性分析表明:与基本遗传算法相比,该算法收敛于全局最优解的概率为1,具有更好的收敛性。并且仿真分析表明:该改进遗传算法与基本遗传算法相比,在BTC译码上改善了约0.4dB的净编码增益。因而所提出的改进遗传算法在译码性能上具有更好的优越性,更适用于FEC译码技术。In order to avoid the defect of the basic genetic algorithm that only competes among one population and has prematurity problem, an improved genetic algorithm is proposed. The improved algorithm increases the diversity of the populations, introduces the concept of similarity and is applied in the interpopulation competition. The convergence analysis shows that the improved algorithm, compared with the basic genetic algorithm, converges to the probability of 1 for the globally optimal solution. And the simulation result shows that, compared with the basic genetic algorithm, the net coding gain（NCG） of the BTC decoding based on the improved algorithm is improved by about 0.4 dB. Therefore, the improved algorithm has a better decoding correction-error performance and can be better suitable for forward error correction （FEC） decoding technologies.

关 键 词：遗传算法 种间竞争 收敛性 净编码增益 

分 类 号：TN911[电子电信—通信与信息系统]