Localized Adaptive Channel and Power Selection With TinyML (LACPSA) in Dense IEEE 802.11 WLANs
Khalid Ibrahim Qureshi, Lu Cheng, Ruoheng Luo, Muhammad Ali Lodhi, Lei Wang · 2024
In dense IEEE 802.11 WLANs, optimized channel allocation and transmission power control are crucial for miti- gating interference and congestion. Traditional approaches face significant challenges due to limited channels and dynamic traffic patterns. To address these issues, we propose a novel scheme wherein TinyML models are directly integrated into access points (APs) for adaptive channel optimization and transmission power control. This distributed approach empowers APs to optimize settings autonomously based on their local information, reducing reliance on central controllers and enhancing scalability. Leveraging compact TinyML models enables rapid decision- making, allowing APs to adapt to network changes promptly. By dynamically adjusting channel optimization and transmission power based on signal strength, interference levels, and traffic load, interference can be minimized, thereby improving network performance. Through extensive simulations, we demonstrate a significant reduction in interference compared to traditional approaches. Our proposed distributed solution enhances network resilience and flexibility, making it more suitable for real-world deployment scenarios where conditions change rapidly. The integration of TinyML into the network presents a promising approach for future WLAN optimization efforts.