Machine Learning in Wireless Networks: Algorithms, Strategies, and Applications

Abstract

The rapid evolution of wireless networks, driven by growing demand for high-speed data, seamless connectivity, and massive device deployment, has posed significant challenges in terms of scalability, resource allocation, interference management, and network automation. Machine Learning (ML), with its data-driven and adaptive approach, offers transformative potential in enhancing wireless network performance and enabling intelligent decision-making. This review explores the integration of ML techniques in wireless networks, covering supervised, unsupervised, and reinforcement learning algorithms. It also examines core strategies for deployment in physical and MAC layers, and a broad range of applications including dynamic spectrum access, load balancing, mobility management, and predictive maintenance. Furthermore, we analyze emerging trends such as federated learning, edge intelligence, and the role of ML in 5G and beyond (6G) networks. The paper concludes by highlighting current challenges and potential future research directions to bridge the gap between ML theory and practical wireless network deployment.