Self-Healing Wireless Networks: AI-Based Fault Detection and Recovery Strategies

Abstract

The explosive increases in the use of wireless networks in modern communication systems dealing with mobile users are maintained while considering the factors like errors and performance dip still present due to ever evolving environments and complex networks of links. This particular study looks into the incorporation of Artificial Intelligence (AI) in intelligent fault detection and recovery systems for enabling self healing features in wireless networks. Constructing a network model with intelligent systems, the proposed method uses machine learning techniques, LSTM, Random forest, and Autoencoders, frameworks capable of detecting and classifying network anomalies in real time with high accuracy. AI methods to augment the available network delay in restoring the network to its operational state after fault recovery is acknowledged to be done optimally. Concerning the detection accuracy, recovery time, and quality of service restoration, the simulation results indicate a drastic improvement using AI-aided methods compared to traditional heuristic approaches. For the considered range of models, the most effective were LSTM and DQ, positioning them as the most suitable large-scale agile wireless environments. Focusing is done in this research on how artificial intelligence can transform wireless networks into innovative self-managing systems that autonomously enhance the system’s reliability and efficiency with a little human intervention.