AI Driven Beamforming technology and Dynamic Spectrum Management for Reliable 5G/6G Long Haul Wireless Networks

Abstract

With the rapid development of 5G networks and the emerging design of 6G networks, the limitations of traditional beamforming and spectrum management approaches have become increasingly apparent, particularly given the demands for ultra reliable and low latency communications. This paper presents simulation based results exploring the integration of artificial intelligence techniques, specifically deep augmented learning (DRL), into adaptive beamforming and dynamic spectrum allocation. The study utilizes multimodal input data (channel states and traffic predictions) to optimize network performance in real time, based on simulations performed using MATLAB and NS3. The results demonstrate a significant improvement in spectrum efficiency, reduced latency, and improved power consumption compared to traditional models. These findings align with the latest standardization efforts of 3GPP Release 18 and the O RAN Alliance, suggesting the feasibility of deploying these frameworks in large scale operational networks. the DRL model with multimodal data significantly improved performance, this came at the cost of increased training time by approximately 10%, representing trade off between learning complexity and operational efficiency.