Correlated Age-of-Information Bandits

TL;DR

This paper studies a multi-armed bandit problem for selecting communication channels in a sensor system, leveraging correlation among channels to improve age-of-information metrics in unknown, stationary, and correlated environments.

Contribution

It introduces a model for correlated channel selection in AoI minimization, analyzing fundamental limits and proposing correlated-aware UCB and Thompson Sampling algorithms.

Findings

Correlation among channels improves channel selection efficiency.

Proposed algorithms outperform traditional methods in correlated environments.

Fundamental performance bounds are established for the problem.

Abstract

We consider a system composed of a sensor node tracking a time varying quantity. In every discretized time slot, the node attempts to send an update to a central monitoring station through one of K communication channels. We consider the setting where channel realizations are correlated across channels. This is motivated by mmWave based 5G systems where line-of-sight which is critical for successful communication is common across all frequency channels while the effect of other factors like humidity is frequency dependent. The metric of interest is the Age-of-Information (AoI) which is a measure of the freshness of the data available at the monitoring station. In the setting where channel statistics are unknown but stationary across time and correlated across channels, the algorithmic challenge is to determine which channel to use in each time-slot for communication. We model the problem as a Multi-Armed bandit (MAB) with channels as arms. We characterize the fundamental limits on the performance of any policy. In addition, via analysis and simulations, we characterize the performance of variants of the UCB and Thompson Sampling policies that exploit correlation.