arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2408.03329cs.NIcs.AIcs.LG
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真实环境中面向高清地图服务质量的基于多智能体方法的覆盖感知与强化学习

Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment

University of Northumbria(诺森比亚大学);University of Warwick(华威大学)

Jeffrey Redondo, Zhenhui Yuan, Nauman Aslam, Juan Zhang

AI总结:

本文针对车载自组织网络中高清地图数据传输的时延与吞吐量保障问题,提出一种应用层 Q-Learning 算法,通过多智能体覆盖感知优化竞争窗口分配,在真实环境中以较少优化需求取得优于 DQN 和 Actor-Critic 的网络性能。

摘要:

优化卸载过程的一种有效方式是尽量缩短传输时间。这一点在车载自组织网络(VANET)中尤为突出,因为车辆需要频繁下载和上传必须持续更新的高清(HD)地图数据。这意味着无线系统必须保证时延和吞吐量要求。为实现这一目标,许多研究者已经探索了标准 IEEE802.11p 中可调节竞争窗口(CW)分配策略。然而,这些实现方案要求对现有标准进行修改,而这并不总是可取的。为解决这一问题,我们提出了一种在应用层运行的 Q-Learning 算法。此外,该算法可以部署在任何无线网络中,从而缓解兼容性问题。与 Deep Q Network(DQN)和 Actor-Critic 算法相比,该解决方案在优化需求相对较少的情况下表现出了更好的网络性能。在多智能体设置下评估该模型时也观察到了相同结果,其性能高于单智能体设置。

英文摘要:

One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires constant updates. This implies that latency and throughput requirements must be guaranteed by the wireless system. To achieve this, adjustable contention windows (CW) allocation strategies in the standard IEEE802.11p have been explored by numerous researchers. Nevertheless, their implementations demand alterations to the existing standard which is not always desirable. To address this issue, we proposed a Q-Learning algorithm that operates at the application layer. Moreover, it could be deployed in any wireless network thereby mitigating the compatibility issues. The solution has demonstrated a better network performance with relatively fewer optimization requirements as compared to the Deep Q Network (DQN) and Actor-Critic algorithms. The same is observed while evaluating the model in a multi-agent setup showing higher performance compared to the single-agent setup.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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