A Review of 6G and AI Convergence: Enhancing Communication Networks With Artificial Intelligence

Abstract

This paper explores the integration of Artificial Intelligence into 6G networks, focusing on optimizing communication, resource allocation, and enhancing security. As communication systems transition from 5G to 6G, Artificial Intelligence’s role in addressing the increasing complexity of network management becomes pivotal. The paper reviews key AI technologies such as machine learning, deep learning, and reinforcement learning, demonstrating their applications in predictive maintenance, traffic management, and energy efficiency optimization. It also highlights how Artificial Intelligence enables intelligent network slicing, spectrum management, and resource allocation through dynamic algorithms. Furthermore, Artificial Intelligence-driven solutions are presented for addressing security concerns in 6G networks, focusing on intrusion detection, anomaly detection, and blockchain-based decentralized security. Challenges such as computational overhead, data privacy, and ethical concerns in implementing Artificial Intelligence in 6G systems are also discussed, along with future directions, including quantum AI and federated learning. This paper provides a comprehensive analysis of how Artificial Intelligence can enhance the capabilities of 6G networks, ensuring improved performance, security, and scalability for future communication technologies.