Compensation of turbulence-induced wavefront aberration with convolutional neural networks for FSO systems

• 作者: Min'an Chen Xianqing Jin Shangbin Li Zhengyuan Xu
• 来源期刊: 中国光学快报（英文版）
• 年，卷（期）: 2021,（11）
• 所属期刊栏目: Fiber Optics and Optical Communications
• 页码范围: 16-21
• 页数: 6页
• 语种: 英文
• DOI: 10.3788/COL202119.110601

摘要

To reduce the atmospheric turbulence-induced power loss,an AlexNet-based convolutional neural network[CNN]for wave-front aberration compensation is experimentally investigated for free-space optical[FSO]communication systems with standard single mode fiber-pigtailed photodiodes.The wavefront aberration is statistically constructed to mimic the received light beams with the Zernike mode-based theory for the Kolmogorov turbulence.By analyzing impacts of CNN structures,quantization resolution/noise,and mode count on the power penalty,the AlexNet-based CNN with 8 bit res-olution is identified for experimental study.Experimental results indicate that the average power penalty decreases to 1.8 dB from 12.4 dB in the strong turbulence.