Elaris Computing Nexus
Adaptive Spectrum Allocation for Low Latency Vehicular Networks Using Deep Reinforcement Learning
Elaris Computing Nexus
Received On : 22 July 2026
Revised On : 29 August 2026
Accepted On : 02 September 2026
Published On : 12 September 2026
Volume 02, 2026
Pages : 190-202
Abstract
The impending deployment of autonomous vehicles has added new requirements about wireless communication in highly dynamic vehicular networks, particularly in terms of reliability and low latency. The allocation of the spectrum is still a difficult problem, as traffic and mobility conditions change continuously, which implies the variation in vehicle density, channel quality, interference, and communication requirements. These variations are not easily handled by the conventional static, rule-based and non-adaptive allocation schemes, affecting the efficient use of the spectrum, communication latency, and packet delivery reliability. To overcome these challenges, this study introduces an Adaptive Deep Reinforcement Learning (ADRL) based dynamic spectrum allocation framework for AVNs. The proposed framework uses a Deep Q-Network (DQN) that, based on observation of the current conditions of the vehicular network (vehicle density, channel quality, level of interference, load of the traffic flow), automatically determines the appropriate spectrum resources. A simulation-based vehicular communication scenario is created to evaluate under various traffic densities and channel-quality levels with dynamic mobility and channel conditions. Experimental analysis takes into account the following factors: spectral efficiency, spectrum utilization, communication latency, packet delivery ratio, reliability, and learning convergence. Results show that the proposed ADRL framework can ensure efficient spectrum allocation and reliable communication in a fast-growing network density and degraded channel environment and has stable convergence characteristics in its training behavior. The results show that adaptive deep reinforcement learning is an effective method to spectrum management in the next generation of autonomous vehicular communication networks that is robust, resource-efficient, and has low latency.
Keywords
Adaptive Spectrum Allocation, Deep Reinforcement Learning, Vehicular Communication Networks, URLLC, Autonomous Vehicles, Dynamic Spectrum Management.
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The dataset used in this study is generated through a simulation-based vehicular communication environment rather than obtained from a publicly available benchmark dataset. Vehicle mobility and traffic scenarios are generated using the open-source Simulation of Urban MObility (SUMO) platform, while the vehicular communication configuration follows the relevant 3GPP V2X evaluation assumptions. SUMO is freely available from its official documentation and repository. The simulation parameters, state-action transitions, and generated performance data used in this study can be made available by the authors upon reasonable request.
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Cite this Article
Cyril Arbel, “Adaptive Spectrum Allocation for Low Latency Vehicular Networks Using Deep Reinforcement Learning”, Elaris Computing Nexus, pp. 190-202, 2026, doi: 10.65148/ECN/2026016.
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© 2026 Cyril Arbel. 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.