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.

翻译：大规模无线网络具有部分可观测与时变动态特性，这一特性对最优控制策略的设计提出了新挑战。本文研究在广义干扰约束下，平均到达率与服务率未知且非平稳的无线网络高效调度算法。该模型体现了现代网络中边缘设备在无线通信方面的真实特征。我们提出一种基于最大权重策略的新型算法MW-UCB，该算法利用滑动窗口上置信界学习非平稳环境下的信道统计量，适用于广义无线网络调度。在平均服务率变化量的温和假设下，MW-UCB被证明可达到吞吐量最优性。具体而言，只要任意时间段内平均服务率的总变差随时间呈次线性增长，MW-UCB就能将系统稳定区域任意逼近于完全已知信道统计量策略类的稳定区域。大量仿真验证了理论结果的有效性并展示了MW-UCB的优异性能。