Artificial Intelligence for Sustainable Wireless Network Management

Abstract

In today’s world, wireless communication networks are essential for applications in business, entertainment, commerce, health, and safety. Over the years, the technology has advanced from the first generation (1G) to the fifth generation (5G). Several nations are currently implementing 5G, and the next generation of wireless communications technologies will be beyond 5G/Sixth Generation (B5G/6G). The advent of B5G/6G networks promises seamless, high-speed connectivity. This will introduce greater complexity and data demands into their operations, requiring significant energy resources. Artificial intelligence (AI) and machine learning (ML) will be essential components of B5G/6G systems to ensure sustainable wireless network management. This chapter presents a survey of AI techniques for B2G/6G wireless network management. The study explores heuristic techniques, such as particle swarm optimization and ant colony optimization, as well as machine learning techniques, including reinforcement learning, deep learning, and federated learning algorithms. The study also examined key technologies for B5G/6G, including terahertz, reconfigurable intelligent surfaces (RIS), ultra-massive MIMO, non-terrestrial networks (NTN), digital twin, holographic beamforming, and AI. The chapter concludes by highlighting the challenges, benefits, lessons learned, and future trends on the subject matter.