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arXiv 2202.09222cs.NI
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无线网络中基于学习的分布式MAC改进AoI

Improving AoI via Learning-based Distributed MAC in Wireless Networks

Yash Deshpande, Onur Ayan, Wolfgang Kellerer

摘要:

在这项工作中,我们考虑了一个远程监控的情况下,多个传感器共享一个无线信道,通过接入点(AP)提供他们的状态更新的过程监视器。此外,我们认为,传感器随机到达和离开网络,因为他们变得活跃和不活跃。传感器的目标是设计一种介质访问策略,以共同最小化远程监视器处其各自进程的长期平均网络\ac{AoI}。为此,我们提出了具体的修改ALOHA-QT算法,分布式介质访问算法,采用策略树(PT)和强化学习(RL),以实现高吞吐量。我们提供了上界的平均网络年龄的信息(AoI)的建议算法,以及指针选择其关键参数。结果表明,该算法减少了平均网络\ac{AoI}超过50%的最先进的静态随机策略,同时成功地调整到网络中的活跃用户的数量不断变化。该算法比ALOHA-QT算法需要更少的内存和计算量,同时在AoI方面表现更好。

英文摘要:

In this work, we consider a remote monitoring scenario in which multiple sensors share a wireless channel to deliver their status updates to a process monitor via an access point (AP). Moreover, we consider that the sensors randomly arrive and depart from the network as they become active and inactive. The goal of the sensors is to devise a medium access strategy to collectively minimize the long-term mean network \ac{AoI} of their respective processes at the remote monitor. For this purpose, we propose specific modifications to ALOHA-QT algorithm, a distributed medium access algorithm that employs a policy tree (PT) and reinforcement learning (RL) to achieve high throughput. We provide the upper bound on the mean network Age of Information (AoI) for the proposed algorithm along with pointers for selecting its key parameter. The results reveal that the proposed algorithm reduces mean network \ac{AoI} by more than 50 percent for state of the art stationary randomized policies while successfully adjusting to a changing number of active users in the network. The algorithm needs less memory and computation than ALOHA-QT while performing better in terms of AoI.

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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