面向模分复用系统的遗传-MIMO均衡参数优化技术
Genetic Algorithm Based MIMO Equalization Parameter Optimization Technology for Mode-Division Multiplexed System
赵天烽 1文峰 1冯变霞 1武保剑 1许渤 1邱昆1
作者信息
- 1. 电子科技大学信息与通信工程学院光纤传感与通信教育部重点实验室,四川成都 611731
- 折叠
摘要
在模分复用系统的数字信号处理单元中,多输入多输出(Multi-Input Multi-Output,MIMO)均衡技术可用来补偿由各类模式相关噪声引起的信号误码率(Bit Error Rate,BER)劣化问题.而MIMO均衡算法的工作性能严重依赖于步长因子μ以及抽头数K,因此在固化均衡器之前,确定MIMO均衡算法中μ-K参数组合的最优值至关重要.为提高参数优化效率,提出了一种基于遗传算法(Genetic Algorithm,GA)的MIMO均衡参数优化方案,即遗传-MIMO(GA-MIMO),在保证最小BER输出的同时降低参数优化过程所需的计算开销.为验证GA-MIMO的工作性能,构建了基于10 km六模光纤的点对点通信实验系统,使用新方案补偿并行通信的六路数据,并与最速下降法和迭代算法进行性能比较.实验结果表明,所提GA方案可实现MIMO均衡中最高99.98%的最优μ-K参数命中率,且GA-MIMO算法的全局搜索性使其相比于最速下降法和迭代算法可最多分别节省86.14%和90.3%的均衡算法调用次数,有效降低了确定最优μ-K参数组合时的计算开销.
Abstract
In the digital signal processing unit of the mode-division multiplexed system,the multi-input and multi-output(MIMO)equalization technology is usually used to compensate for the signal bit error rate(BER)degradation disturbed by various mode-dependent noises.The performance of MIMO equalization algorithm depends heavily on the step size factor μ and the number of taps K,so before welding the equalizers,it's important to determine the optimal value of μ-K combination in MIMO equalization algorithm.A genetic algorithm(GA)based MIMO equalization parameter optimization scheme,name-ly GA-MIMO,is proposed to improve the efficiency of the parameter optimization,which is used to reduce the computational costs required during parameter optimization with the minimum BER output.In order to verify the performance of GA-MI-MO,a point-to-point communication experimental system based on 10 km six-mode fiber is constructed.The new scheme is used to compensate the parallelly transmitted six-channel data,and the performance is compared with the steepest descent method and iterative algorithm.The experimental results show that the proposed GA scheme achieves the hit rate of the opti-mal μ-K parameters in MIMO equalization up to 99.98%,and the global search function of GA algorithm helps save the num-ber of calls to the equalization algorithm of 86.14%and 90.3%compared with the steepest descent algorithm and iterative al-gorithm,respectively,effectively reducing the computational cost of locating μ-K parameters.
关键词
模分复用/多输入多输出/遗传算法/少模光纤/最小均方误差Key words
mode-division multiplexing/multi-input and multi-output/genetic algorithm/few-mode fiber/least mean square引用本文复制引用
基金项目
国家重点研发计划(2018YFB1801001)
出版年
2024
国家科技期刊平台
NSTL
万方数据