AI-powered framework for advancing 5G and 6G wireless communication

Abstract

A shift to 5G to 6G networks would provide better connectivity, transmission capacity, security, and latency, but there are still challenges such as dynamic resource allocation, energy consumption, network optimisation, and cybersecurity issues. This research presents an artificial intelligence (AI)-based approach to enhance the accuracy, efficiency and security of 5G and future 6G wireless networks. Adaptive spectrum and resource allocation is done using deep reinforcement learning (DRL), and AI traffic forecasting and dynamic beamforming helps reduce latency, boost throughput and ensure quality of service (QoS). Federated learning (FL) trains models at the distributed sites while minimizing the amount of raw data transmission, and generative adversarial networks (GANs) create representative samples of data for network and cybersecurity analysis. The anomaly detection feature powered by AI, along with the blockchain-based authentication, helps enhance access control and threat detection. The framework was tested and analyzed in a simulation environment built in Python and in comparison to traditional optimization. It performed an F1-score of 0.94, versus 0.82; AUC–ROC of 0.96, versus 0.86; and resource-allocation success rate (91% versus 75%). These results represent the relative increases of 14.6%, 11.6% and 21.3%. The results suggest that AI-driven coordinated optimisation could lead to a more adaptive management of resources, threat detection, and efficient operation of networks compared with traditional methods, paving the way for intelligence, security and energy-efficient 6G communication systems.