Learning to Schedule in Non-Stationary Wireless Networks With Unknown Statistics
Learning to Schedule in Non-Stationary Wireless Networks With Unknown Statistics
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DOI：
10.1145/3565287.3610258
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Quang Minh Nguyen;E. Modiano
中科院分区：
文献类型：
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作者：
Quang Minh Nguyen;E. Modiano
具有部分可观测和时变动态特性的大规模无线网络的出现给最优控制策略的设计带来了新的挑战。本文研究了在广义干扰约束下，平均到达速率和平均服务速率未知且非平稳的无线网络中的高效调度算法。该模型模拟了现代网络中边缘设备的无线通信特性。提出了一种基于最大权值策略的广义无线网络调度算法MW-UCB，该算法利用滑动窗口置信上界学习非平稳信道的统计信息。MW-UCB是可证明的吞吐量最优的平均服务率的变化在温和的假设下。具体而言，只要在任何时间段内的平均服务速率的总变化增长次线性的时间，我们表明，MW-UCB可以实现的稳定区域任意接近的稳定区域的政策类的充分知识的信道统计。大量的仿真验证了我们的理论结果，并证明了MW-UCB的良好性能。
The emergence of large-scale wireless networks with partially-observable and time-varying dynamics has imposed new challenges on the design of optimal control policies. This paper studies efficient scheduling algorithms for wireless networks subject to generalized interference constraint, where mean arrival and mean service rates are unknown and non-stationary. This model exemplifies realistic edge devices' characteristics of wireless communication in modern networks. We propose a novel algorithm termed MW-UCB for generalized wireless network scheduling, which is based on the Max-Weight policy and leverages the Sliding-Window Upper-Confidence Bound to learn the channels' statistics under non-stationarity. MW-UCB is provably throughput-optimal under mild assumptions on the variability of mean service rates. Specifically, as long as the total variation in mean service rates over any time period grows sub-linearly in time, we show that MW-UCB can achieve the stability region arbitrarily close to the stability region of the class of policies with full knowledge of the channel statistics. Extensive simulations validate our theoretical results and demonstrate the favorable performance of MW-UCB.