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.

对于具有部分可观察和时变动力学的大规模无线网络，本文研究了一种面向广义干扰约束的无线网络的高效调度算法，其中平均到达率和平均服务率是未知的和非平稳的。我们提出了一种新颖的算法 MW-UCB 用于广义无线网络调度，它基于最大权重策略，并利用滑动窗口上限置信界来学习通道在非平稳情况下的统计特性。在平均服务率的可变性方面满足较弱的假设条件下，MW-UCB 在吞吐量上是最优的。具体而言，只要平均服务率在任何时间段内的总变化量在时间上增长的次线性，我们表明 MW-UCB 可以实现稳定区域任意靠近拥有完全通道统计信息的策略类的稳定区域。大量模拟验证了我们的理论结果，并展示了 MW-UCB 的良好性能。