DOI: 10.11648/j.ajnc.20261502.12 ISSN: 2326-8964

Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review

Fumlack George, Jean Ntsama
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The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.

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