Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems
Abstract
:1. Introduction
2. Literature Survey
3. Problem Statement
4. Proposed Framework
4.1. Adaptive Fuzzy Logic Deep-Learning Equalizer (AFL-DLE)
4.2. Optimization Using Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA)
4.3. Experimental Configuration
5. Results and Discussion
5.1. Bit Error Rate
5.2. Distortion Rate
5.3. Computational Complexity
5.4. Transmission Rate
Source | Transmission Rate (%) |
---|---|
APNN [33] | 69 |
TFCNN [34] | 85 |
DSANet [35] | 65 |
CE-LSTM [36] | 72 |
AFL-DLE [Proposed] | 99 |
5.5. Computation Cost
5.6. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Rajalakshmi, R.; Pothiraj, S.; Mahdal, M.; Elangovan, M. Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems. Sensors 2023, 23, 5418. https://doi.org/10.3390/s23125418
Rajalakshmi R, Pothiraj S, Mahdal M, Elangovan M. Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems. Sensors. 2023; 23(12):5418. https://doi.org/10.3390/s23125418
Chicago/Turabian StyleRajalakshmi, Radhakrishnan, Sivakumar Pothiraj, Miroslav Mahdal, and Muniyandy Elangovan. 2023. "Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems" Sensors 23, no. 12: 5418. https://doi.org/10.3390/s23125418
APA StyleRajalakshmi, R., Pothiraj, S., Mahdal, M., & Elangovan, M. (2023). Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems. Sensors, 23(12), 5418. https://doi.org/10.3390/s23125418