• yuvraj singh
• Dr. ANUBHUTI KHARE

Abstract Vehicular communication systems are evolving rapidly with the emer- gence of Vehicle-to-Everything (V2X) technologies. However, reliable communica- tion in dense urban environments remains challenging due to dynamic blockages, frequent handovers, and misaligned beams. Traditional beam alignment approaches suffer from high latency and reduced accuracy, which limits their applicability in real-time vehicular scenarios. To address these issues, this paper investigates the integration of Vehicular Edge Intelligence (VEI) for optimizing 3D beam align- ment and mitigating blockage in V2X communication. By leveraging edge-assisted machine learning models, vehicles can predict link quality, adapt beam direction, and proactively manage mobility-induced blockages. The proposed approach aims to improve reliability, reduce latency, and enhance spectral efficiency compared to conventional methods. Simulation results demonstrate the effectiveness of VEI- driven beam alignment in maintaining stable connectivity, achieving lower outage probability, and enabling seamless vehicular communication under high-mobility conditions. This work contributes toward the development of intelligent, adaptive, and resilient V2X networks that are essential for autonomous driving and future 6G-enabled transportation systems.

Vehicular Edge Intelligence, V2X Communication, Beam Alignment, Blockage Mitigation, 3D Beamforming, Autonomous Vehicles.

"Integration of Vehicular Edge Intelligence for 3D Beam Alignment and Blockage Mitigation in V2X Communication", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.g167-g174, November-2025, Available :http://www.jetir.org/papers/JETIR2511667.pdf

"Integration of Vehicular Edge Intelligence for 3D Beam Alignment and Blockage Mitigation in V2X Communication", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppg167-g174, November-2025, Available at : http://www.jetir.org/papers/JETIR2511667.pdf