TELEMETRY-DRIVEN SELF-HEALING WI-FI MESH NETWORKS FOR ULTRA-DENSE DEVICE ENVIRONMENTS

Abstract

Ultra-dense device environments present unprecedented challenges for Wi-Fi mesh networks, including interference management, client roaming inefficiencies, and dynamic congestion patterns. Traditional mesh architectures rely on static configurations that fail to adapt to real-time network conditions, resulting in degraded performance and connectivity issues. This research proposes a telemetry-driven self-healing framework that leverages AI-assisted analysis to enable autonomous network optimization in ultra-dense deployments. Our system continuously monitors RF conditions, client behaviors, and traffic patterns to predict packet loss, optimize channel assignments, and intelligently steer client devices. The framework incorporates machine learning algorithms for predictive analytics, congestion-aware steering mechanisms, and automated healing protocols that respond to network anomalies without human intervention. Experimental validation in environments with over 200 concurrent devices demonstrates significant improvements in throughput stability, reduced packet loss, and enhanced roaming performance. The proposed architecture extends Wi-Fi 7 mesh capabilities through intelligent telemetry utilization, providing scalable solutions for stadiums, hospitals, campuses, and smart building deployments where device density exceeds traditional network design parameters.