6G NETWORKS: ENABLING TECHNOLOGIES USING MATLAB

Abstract

The sixth generation (6G) wireless network is expected to provide ultra-high data rates, ultra-low latency, massive connectivity, intelligent resource management, and ubiquitous coverage. This paper presents an AI-enabled 6G framework that integrates Machine Learning (ML), Deep Learning (DL), Federated Learning (FL), Deep Reinforcement Learning (DRL), Explainable AI (XAI), and Generative AI (GenAI) with key 6G technologies such as THz communication, ultra massive MIMO, RIS, edge computing, and intelligent connectivity. MATLAB-based simulations are used to evaluate the performance of the proposed AI techniques. The results show a traffic prediction RMSE of 8.95 Mbps, channel estimation RMSE of 0.0496, and DL classification accuracy of 92.57%. Federated Learning achieves a traffic prediction RMSE of 8.89 Mbps while maintaining local data privacy. DRL provides adaptive bandwidth allocation based on network load, while XAI identifies the most influential traffic features. GenAI further demonstrates automated 6G network configuration and service orchestration. The results indicate that AI integration can improve network intelligence, resource optimization, security, adaptability, and operational efficiency, providing a promising foundation for future 6G applications including smart cities, autonomous transportation, industrial automation, healthcare, IoT, immersive XR, and digital twins.