Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

Abstract

In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications.