Channel estimation-based time-frequency neural network for post-equalization in underwater visible light communication  

作　　者：Haoyu Zhang Li Yao Chaoxu Chen Yuan Wei Chao Shen Jianyang Shi Junwen Zhang Ziwei Li Nan Chi 张昊宇;姚力;陈超旭;魏圆;沈超;施剑阳;张俊文;李子薇;迟楠(Key Laboratory for Information Science of Electromagnetic Waves(MoE),Department of Communication Science and Engineering,School of Information Science and Technology,Fudan University,Shanghai 200433,China;Shanghai CIC of LEO Satellite Communication Technology,Fudan University,Shanghai 200433,China;Shanghai ERC of LEO Satellite Communication and Application,Fudan University,Shanghai 200433,China)

机构地区：[1]Key Laboratory for Information Science of Electromagnetic Waves(MoE),Department of Communication Science and Engineering,School of Information Science and Technology,Fudan University,Shanghai 200433,China [2]Shanghai CIC of LEO Satellite Communication Technology,Fudan University,Shanghai 200433,China [3]Shanghai ERC of LEO Satellite Communication and Application,Fudan University,Shanghai 200433,China

出　　处：《Chinese Optics Letters》2024年第6期116-122,共7页中国光学快报（英文版）

基　　金：supported by the National Key Research and Development Program of China (No.2022YFB2802803);the National Natural Science Foundation of China (Nos.61925104,62031011,and 62201157)。

摘　　要：This Letter proposes a post-equalizer for underwater visible light communication(UVLC) systems that combines channel estimation and joint time-frequency analysis, named channel-estimation-based bandpass variable-order time-frequency network(CBV-TFNet). By utilizing a bandpass variable-order loss function with communication prior knowledge, CBVTFNet enhances communication performance and training stability. It enables lightweight implementation and faster convergence through a channel estimation-based mask. The superior performance of the proposed equalization method over Volterra and deep neural network(DNN)-based methods has been studied experimentally. Using bit-power loading discrete multitone (DMT) modulation, the proposed method achieves a transmission bitrate of 4.956 Gbps through a 1.2 m underwater channel utilizing only 38.15% of real multiplication calculations compared to the DNN equalizer and achieving a bitrate gain of440 Mbps and a significantly larger dynamic range over the LMS-Volterra equalizer. Results highlight CBV-TFNet's potential for future post-equalization in UVLC systems.

关 键 词：underwater visible light communication channel estimation post-equalizer neural network 

分 类 号：TN929.1[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]