AI-Based Wi-Fi Performance Prediction and Intelligent Network Optimization System (A Proposed Framework)

The performance of a Wi-Fi network depends on many interacting factors such as signal strength, interference, channel utilization, traffic load and the number of connected devices. Conventional management is largely static and reactive, so problems are noticed only after users are affected, and prediction and optimization are usually treated as separate tasks. This paper proposes an integrated framework that uses machine learning to predict Wi-Fi performance from measured network parameters, classifies the network condition, and generates optimization recommendations such as channel selection, transmit power adjustment and traffic management. The workflow, input parameters, outputs, system architecture and two algorithms of the framework are described. The work is at the design stage: the architecture and methodology are finalized, while model development and experimental evaluation are planned, and no experimental results are reported in this paper.