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.

This paper develops a novel approach for reliable vehicle-to-vehicle (V2V) communication in various environments. A switched beam antenna is deployed at the transmitting and receiving points, with a beam management system that concentrates the power in each beam using a low-computation algorithm and a potential mathematical model. The algorithm is designed to be flexible for various environments faced by vehicles. Additionally, an anti-failure system is proposed in case the intelligent transportation system (ITS) system fails to retrieve real-time Packet Delivery Ratio (PDR) values related to traffic density. Performance metrics include the time to collision in seconds, the bit error rate (BER), the packet error rate (PER), the average throughput (Mbps), the beam selection probability, and computational complexity factors. The proposed system is compared with traditional systems. Extensive experiments, simulations, and comparisons show that the proposed approach is excellent and reliable for vehicular systems. The proposed study demonstrates an average throughput of 1.7 Mbps, surpassing conventional methods’ typical throughput of 1.35 Mbps. Moreover, the bit error rate (BER) of the proposed study is reduced by a factor of 0.1. Additionally, the proposed framework achieves a beam power efficiency of touching to 100% at computational factor of 34. These metrics indicate that the proposed method is both efficient and sufficiently robust.

The millimeter-wave of 5G will usher in a new era in Vehicle-to-Vehicle (V2V) communication. The ensuing radiation from a millimeter-wave of 5G bounces off most visible things, creating enriched multi-directional environments, particularly in sub-urban scenarios. Physical impediments were once primarily connected with signal attenuation; nevertheless, their existence now introduces complicated, non-linear phenomena such as reflections and scattering. As a result of the impediments faced, a multipath propagation environment emerges, suggesting the presence of concealed spatial information within the received signal for a dense vehicular environment. The key contributions of this research are to discuss and evaluate a self-proposed deep neural network for the beamformed fingerprint location problem in connected cars. Training of deep learning model and simulation environment has been performed using AMD Ryzen 7 GPU environment. Results show that in a realistic outdoor sub-urban scenario with predominantly non-line-of-sight (NLoS) positions, average estimation errors of less than 1.69 m can be achieved, paving the way for novel positioning systems beneficial for V2V links with low computational power. Furthermore, the self-proposed deep learning model is compared with the long-short-term memory (LSTM) in terms of computational complexity. Self-proposed DNN outperform LSTM in terms of training time by 50 min.