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RORA: Reinforcement learning based optimal distributed resource allocation strategies in vehicular cognitive radio networks for 6G
Vehicular Communications ( IF 5.8 ) Pub Date : 2025-01-20 , DOI: 10.1016/j.vehcom.2025.100882
Mani Shekhar Gupta, Akanksha Srivastava, Krishan Kumar

The next generation (5G/B5G) vehicular cognitive radio networks (VCRNs) flag the track to intelligence-based autonomous driving in the initiation of future wireless networking and make daily vehicular operation more convenient, greener, efficient, and safer. However, with the continuous evolution of vehicles, the vehicular network becomes large-scale, dynamic, and heterogeneous, making it tough to fulfill the strict necessities, such as high security, resource allocation, massive connectivity, and ultralow latency. The combination of cognitive radio (CR) networks (different network coexistence) and machine learning (ML) has arisen as an influential artificial intelligence (AI) approach to make both the communication system and vehicle more adaptable and efficient. Naturally, applying ML to VCRNs has become an active research area and is being extensively considered in industry and academia. In this work, a reinforcement learning (RL) based optimal resource allocation (RORA) technique is proposed to solve the myopic decision-making problem by an autonomous vehicle (RL agent) takes its action to select the power level and optimal sub-band and maximize long-term rewards with a maximum payoff in VCRNs. The aim of this work is to design and implement an intelligent, resource allocation framework that ensures efficient and adaptive spectrum utilization while minimizing communication latency, energy consumption, and transmission cost in VCRNs. As a schema for the realization and capabilities evaluations, the CR networks consisting of LTE cellular network inter-working with Wi-Fi network with constant inter-space between Wi-Fi access points (APs) installed along the pathway is analysed. This framework is further analysed with variable inter-space between Wi-Fi APs. The key research problem addressed in this work is the challenge of optimizing spectrum and power allocation in highly dynamic vehicular environments characterized by rapid mobility, fluctuating network conditions, and interference from multiple vehicular CR nodes. The results show that the proposed RORA technique is more operative and outperforms other resource allocation schemes in terms of prediction accuracy and throughput.

中文翻译:


RORA:基于强化学习的 6G 车载认知无线电网络最优分布式资源分配策略



下一代 (5G/B5G) 车载认知无线电网络 (VCRN) 为未来无线网络的启动开启了智能化自动驾驶的道路,使日常车辆运营更加便捷、绿色、高效和安全。然而,随着车辆的不断发展,车载网络变得大规模、动态和异构,难以满足高安全性、资源分配、海量连接和超低延迟等严格要求。认知无线电 (CR) 网络(不同的网络共存)和机器学习 (ML) 的结合已成为一种有影响力的人工智能 (AI) 方法,可以提高通信系统和车辆的适应性和效率。自然,将 ML 应用于 VCRN 已成为一个活跃的研究领域,并在工业界和学术界被广泛考虑。在这项工作中,提出了一种基于强化学习 (RL) 的最佳资源分配 (RORA) 技术来解决近视决策问题,自动驾驶汽车 (RL 代理) 采取行动选择功率水平和最佳子带,并在 VCRN 中以最大回报最大化长期奖励。这项工作的目的是设计和实施一个智能的资源分配框架,以确保高效和自适应的频谱利用,同时最大限度地减少 VCRN 中的通信延迟、能耗和传输成本。作为实现和能力评估的方案,分析了由 LTE 蜂窝网络与 Wi-Fi 网络互连组成的 CR 网络,该网络在沿路径安装的 Wi-Fi 接入点 (AP) 之间具有恒定的间隔。该框架通过 Wi-Fi 接入点之间的可变间隔进行了进一步分析。 这项工作解决的关键研究问题是在高度动态的车辆环境中优化频谱和功率分配的挑战,其特点是快速移动、波动的网络条件和来自多个车辆 CR 节点的干扰。结果表明,所提出的 RORA 技术更具操作性,并且在预测准确性和吞吐量方面优于其他资源分配方案。
更新日期:2025-01-20
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