Blind Optimal User Association in Small-Cell Networks

TL;DR

This paper introduces a novel online learning framework for optimal user association in small-cell networks, effectively adapting to dynamic traffic demands and minimizing various load-related cost functions.

Contribution

It proposes PerOnE, a simple online algorithm with no-regret guarantees, and introduces a new periodic benchmark for online convex optimization problems.

Findings

PerOnE adapts to traffic fluctuations effectively.

The algorithm enjoys no-regret performance guarantees.

Validated results on real traffic data support the approach.

Abstract

We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of $\alpha$-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of cost-efficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.