Elaris Computing Nexus
Deep Reinforcement Learning based Adaptive Beamforming for High Mobility Vehicular 6G Networks
Elaris Computing Nexus
Received On : 16 November 2025
Revised On : 23 December 2025
Accepted On : 10 January 2026
Published On : 29 January 2026
Volume 02, 2026
Pages : 013-026
Abstract
One of the most important application areas of sixth-generation (6G) wireless networks is high-mobility vehicular communication, where the speed of channel variations and high rates of beam misalignment severely affect the performance of the existing beamforming methods. In this paper, the authors suggest a deep reinforcement learning (DRL)-enabled adaptive beamforming algorithm of smart antenna systems that can work in the 6G high-mobility vehicular setting. The given method develops beam selection and beam weight optimization into a decision sequence, in which an agent based on DRL acquires dynamically optimum beamforming actions through interacting with a time-varying vehicular channel. In contrast to the traditional or codebook-based beamforming schemes, the discussed one can change in time depending on the vehicle speed, direction, and channel conditions without the need to estimate the channel state information explicitly. The realistic vehicular channel model is used to test the system performance in various mobility conditions. The results of the simulation prove that the proposed DRL-based beamforming algorithm has better beam alignment accuracy, achieves high signal-to-noise ratio, and better spectral efficiency than traditional maximum ratio transmission and fixed beamforming baselines. The findings validate the utility of deep reinforcement learning in facilitating intelligent, low-latency, and strong beamforming solutions to high-mobility vehicular communications in 6G networks in the future.
Keywords
Deep Reinforcement Learning, Adaptive Beamforming, Smart Antenna Systems, High-Mobility Vehicular Communications, 6G Wireless Networks.
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CRediT Author Statement
The author reviewed the results and approved the final version of the manuscript.
Conceptualization: Anandakumar Haldorai and Babitha Lincy R; Methodology: Anandakumar Haldorai and Babitha Lincy R; Software: Anandakumar Haldorai; Data Curation: Anandakumar Haldorai and Babitha Lincy R; Writing- Original Draft Preparation: Anandakumar Haldorai; Visualization: Anandakumar Haldorai and Babitha Lincy R; Investigation: Anandakumar Haldorai and Babitha Lincy R; Supervision: Anandakumar Haldorai; Validation: Anandakumar Haldorai and Babitha Lincy R; Writing- Reviewing and Editing: Anandakumar Haldorai and Babitha Lincy R; All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Anandakumar Haldorai for this research completion and support.
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Cite this Article
Anandakumar Haldorai and Babitha Lincy R, “Deep Reinforcement Learning based Adaptive Beamforming for High Mobility Vehicular 6G Networks”, Elaris Computing Nexus, pp. 013-026, 2026, doi: 10.65148/ECN/2026002.
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© 2026 Anandakumar Haldorai and Babitha Lincy R. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.