Statistical Learning Models for Performance Optimization-AI Driven Wireless Communication Networks

Abstract

The rapid expansion of 5G and emerging 6G networks has increased data traffic, user demands, and system complexity. Artificial Intelligence (AI) and statistical learning models are critical for optimizing latency, throughput, spectrum efficiency, and energy consumption. This chapter examines integrating regression models, Bayesian inference, SVMs, ensemble learning, and deep learning into AI-driven wireless systems. Performance optimization strategies include resource allocation, traffic prediction, QoS enhancement, and dynamic spectrum management, while addressing challenges such as scalability, interpretability, and real-time adaptability. Future research directions focus on AI-statistical learning convergence for next-generation networks.