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

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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,CBV-TFNet enhances communication performance and training stability.It enables lightweight implementation and faster con-vergence 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 of 440 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.

underwater visible light communicationchannel estimationpost-equalizerneural network

张昊宇、姚力、陈超旭、魏圆、沈超、施剑阳、张俊文、李子薇、迟楠

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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

2024

10.3788/COL202422.060602

中国光学快报(英文版)
中国光学学会 中国科学院上海光学精密机械研究所

中国光学快报(英文版)

CSTPCD
影响因子：1.305
ISSN：1671-7694
年,卷(期)：2024.22(6)