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首页> 外文期刊>International Journal of Computer Network and Information Security >Adaptive Population Sizing Genetic Algorithm Assisted Maximum Likelihood Detection of OFDM Symbols in the Presence of Nonlinear Distortions
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Adaptive Population Sizing Genetic Algorithm Assisted Maximum Likelihood Detection of OFDM Symbols in the Presence of Nonlinear Distortions

机译：存在非线性失真的自适应种群大小遗传算法辅助的OFDM符号最大似然检测

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摘要

This paper presents Adaptive Population Sizing Genetic Algorithm (AGA) assisted Maximum Likelihood (ML) estimation of Orthogonal Frequency Division Multiplexing (OFDM) symbols in the presence of Nonlinear Distortions. The proposed algorithm is simulated in MATLAB and compared with existing estimation algorithms such as iterative DAR, decision feedback clipping removal, iteration decoder, Genetic Algorithm (GA) assisted ML estimation and theoretical ML estimation. Simulation results proved that the performance of the proposed AGA assisted ML estimation algorithm is superior compared with the existing estimation algorithms. Further the computational complexity of GA assisted ML estimation increases with increase in number of generations or/and size of population, in the proposed AGA assisted ML estimation algorithm the population size is adaptive and depends on the best fitness. The population size in GA assisted ML estimation is fixed and sufficiently higher size of population is taken to ensure good performance of the algorithm but in proposed AGA assisted ML estimation algorithm the size of population changes as per requirement in an adaptive manner thus reducing the complexity of the algorithm.
机译：本文提出了在存在非线性失真的情况下，正交频分复用（OFDM）符号的自适应种群规模遗传算法（AGA）辅助最大似然（ML）估计。该算法在MATLAB中进行了仿真，并与现有的估计算法进行了比较，如迭代DAR，决策反馈削波去除，迭代解码器，遗传算法辅助的ML估计和理论ML估计。仿真结果表明，所提出的AGA辅助ML估计算法的性能优于现有估计算法。此外，GA辅助ML估计的计算复杂度随着世代数或/和种群大小的增加而增加，在提出的AGA辅助ML估计算法中，种群大小是自适应的，并且取决于最佳适应性。 GA辅助ML估计中的种群大小是固定的，并且采用足够大的种群大小来确保算法的良好性能，但是在AGA辅助ML估计算法中，种群大小会根据需求以自适应方式变化，从而降低了算法的复杂性算法。

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