Deep learning-based symbol detection algorithm in IMDD-OOFDM system  

作　　者：Zhang Huibin Li Tianzhu Liu Haojiang Li Zhuotong 

机构地区：[1]Key Laboratory of Information Photonics and Optical Communications,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2022年第6期36-45,共10页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61831003).

摘　　要：In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing(IMDD-OOFDM) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network(ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio(SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion.

关 键 词：echo state network(ESN) channel estimation channel compensation symbol synchronization training sequence 

分 类 号：TN929.1[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]