An Adaptive Channel Allocation and Interference Mitigation Framework for Dense Wireless Networks
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Abstract
The rapid growth of wireless devices and demand for higher data rates have caused significant spectrum congestion, interference, and performance degradation in Wi-Fi 6 (802.11ax) networks. Traditional static or heuristic-based channel assignment approaches struggle to adapt to dynamic and dense wireless environments, resulting in inefficient spectrum utilization. To address these challenges, this study proposes an adaptive hybrid framework that integrates machine learning with evolutionary optimization. This approach combines predictive intelligence with self-adjustment capabilities, overcoming the limitations of conventional methods. The proposed system consists of three key components: a real-time monitoring agent, a Gradient Boosting Regressor (GBR)-based interference prediction module, and an energy-efficient dynamic channel manager. The channel manager selects optimal channels by considering signal strength, noise levels, user density, and switching overhead. Simulation results using NS-3 in a university campus scenario with 20 access points and 400 clients demonstrate significant performance improvements. The framework reduces interference by 42% and increases throughput by 38% compared to traditional methods. Additionally, it maintains low latency and ensures minimal service disruption during channel switching. These findings highlight the effectiveness of combining AI-driven predictive analytics with adaptive control for real-time interference management in dense Wi-Fi 6 environments
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