A Novel Framework for Enhancing WiFi Performance Through Adaptive Channel Allocation and AI-Driven Interference Mitigation

Abstract

With the exponential growth of connected devices, WiFi networks face increasing congestion, interference, and inefficiencies in spectrum utilization. This paper presents a novel framework that integrates adaptive channel allocation with AIdriven interference mitigation to enhance WiFi performance in dense environments. By leveraging machine learning algorithms, the proposed system dynamically adjusts channel assignments based on real-time network traffic, environmental conditions, and historical data trends. Additionally, an interference-aware optimization model is developed to minimize packet collisions and latency. Experimental simulations demonstrate that the framework significantly improves throughput, reduces congestion, and enhances overall Quality of Service (QoS) compared to conventional static allocation methods. This research provides a scalable solution for next-generation wireless communication, offering a robust approach to optimizing spectrum efficiency in modern WiFi deployments.