Optimizing Hybrid LiFi Communication Systems Using Fuzzy Reinforcement Learning for Enhanced Network Performance

Abstract

Light Fidelity (LiFi) technology has emerged as a pivotal solution for high-speed data transmission in modern communication networks. However, its limitations, such as signal obstruction and coverage gaps, necessitate integration with hybrid systems to ensure seamless connectivity. This study introduces a novel Fuzzy Reinforcement Learning (FRL) algorithm to optimize hybrid LiFi communication systems, addressing critical challenges like handover inefficiency, load imbalance, and dynamic environment adaptation. The proposed FRL framework combines fuzzy logic to manage uncertainties in user mobility and channel conditions with reinforcement learning to dynamically adapt network parameters, ensuring optimal performance. Through comprehensive simulations and real-world validations, the hybrid system demonstrates significant improvements in throughput (4.8 Gbps), handover latency (20 ms), and coverage (100% user connectivity) compared to standalone LiFi and traditional RF-based networks. Key contributions include non-linear decision-making, long-term performance optimization, and scalable deployment strategies for next-generation wireless systems. The results highlight the potential of FRL-optimized hybrid LiFi networks to overcome current bandwidth constraints, offering a robust solution for 6G and IoT applications. This work bridges the gap between theoretical advancements and practical implementation, paving the way for energy-efficient, high-performance communication systems.