Learning to Schedule in Non-Stationary Wireless Networks With Unknown Statistics

TL;DR

This paper introduces MW-UCB, a scheduling algorithm for non-stationary wireless networks that learns channel statistics on-the-fly, achieving near-optimal throughput despite unknown and time-varying conditions.

Contribution

The paper proposes MW-UCB, a novel Max-Weight based algorithm utilizing Sliding-Window UCB for non-stationary wireless network scheduling, with proven throughput optimality.

Findings

MW-UCB achieves near-optimal stability region under sub-linear variation in service rates.

The algorithm outperforms existing methods in simulations with non-stationary channels.

Theoretical analysis confirms throughput optimality under mild non-stationarity assumptions.

Abstract

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.