OPTIMIZATION STRATEGIES FOR WIRELESS COMMUNICATION NETWORKS BASED ON MACHINE LEARNING

With the rapid development of wireless communication technology, higher demands have been placed on network performance and resource utilization efficiency. This paper deeply explores the application of machine learning in wireless communication network optimization, analyzes the challenges faced by current wireless communication networks, such as scarce spectrum resources, unbalanced network load, and difficult energy consumption management. From a technical perspective, it elaborates on the application methods of machine learning in key fields such as spectrum management, mobility management, and energy consumption management, including the detailed implementation of machine learning-based spectrum allocation algorithms, mobility prediction models, and energy consumption optimization strategies. The paper also delves into the underlying principles, parameter tuning, and performance evaluation of relevant algorithms. Through theoretical analysis and practical case verification, this paper demonstrates how machine learning technologies effectively enhance the performance of wireless communication networks and achieve efficient resource utilization, providing theoretical support and practical guidance for the development of future wireless communication networks.