光学学报, 2017, 37 (1): 0106006, 网络出版: 2017-01-13   

基于群智能算法的光OFDM系统PAPR抑制

PAPR Reduction in Optical OFDM Systems Based on Swarm Intelligence Algorithms
作者单位
河北工业大学电子信息工程学院天津市电子材料与器件重点实验室, 天津 300401
摘要
针对相干光正交频分复用(OFDM)系统中峰值平均功率比(PAPR)高的问题, 对粒子群算法(PSO)、蝙蝠算法(BA)和鸟群算法(BSA)等几种群智能算法进行了研究, 采用群智能算法优化OFDM符号的子载波相位, 达到降低PAPR的目的。同时, 通过动态调整认知系数和学习因子, 分别对蝙蝠算法和鸟群算法进行了改进。对100 Gb/s、二进制正交振幅调制(4QAM)的相干光OFDM系统的仿真实验表明, PSO、BA、BSA三种智能算法都能有效降低系统的PAPR, 且改进BSA和改进BA与原始信号相比可使PAPR分别降低约5.11 dB、5.48 dB, 具有更好的抑制效果; 采用群智能算法优化后, 系统误码率性能也得到提高, 且随着光信噪比的增大, 性能提高更加明显。
Abstract
Aiming to reduce peak-to-average power ratio (PAPR) in coherent optical orthogonal frequency division multiplexing (OFDM) systems, several swarm intelligence algorithms are investigated, including particle swarm optimization algorithm (PSO), bat algorithm (BA) and birds swarm algorithm (BSA), and these algorithms can be used for optimizing the sub-carrier phase of OFDM symbols. Moreover, BA and BSA are modified by changing cognitive coefficient and learning factor dynamically. The simulation is carried out in a 100 Gb/s, binary quadrature amplitude modulated (4QAM) coherent optical OFDM system and the results show that PSO, BA and BSA can effectively reduce PAPR of the system, and the PAPRs for the modified BSA and the modified BA can be reduced by about 5.11 dB and 5.48 dB, respectively, compared with that of the original OFDM. The modified intelligence algorithms show better performance. The intelligence algorithms can also improve the bit error ratio performance, and the performance improvement is more obvious when the optical signal to noise ratio increases.

刘剑飞, 王少影, 曾祥烨, 卢嘉, 王蒙军. 基于群智能算法的光OFDM系统PAPR抑制[J]. 光学学报, 2017, 37(1): 0106006. Liu Jianfei, Wang Shaoying, Zeng Xiangye, Lu Jia, Wang Mengjun. PAPR Reduction in Optical OFDM Systems Based on Swarm Intelligence Algorithms[J]. Acta Optica Sinica, 2017, 37(1): 0106006.

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