Nonlinear Compensation based on Function Link Neural Network in Underwater Visible Light Communicaion System

Jin, Ruizhe; Lin, Xianhao; Cai, Jifan; Qin, Guojin; Chi, Nan

doi:10.1109/icocn55511.2022.9901057

Cited by 1 publication

(1 citation statement)

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“…Jin et al [ 13 ] proposed a “Functional Link Neural Network” as a post-equalization technique in underwater visible light communication technology for nonlinear equalization. Du et al [ 14 ] presented an in-depth analysis of intelligent hardware and network layer techniques for an AI-enabled advanced UVLC system.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems

Rajalakshmi¹,

Sivakumar

Mahdal

et al. 2023

Sensors

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Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.

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Section: Literature Surveymentioning

confidence: 99%