Age of Information Optimization in Multi-Channel Network with Sided Information

TL;DR

This paper addresses optimizing Age of Information in a multi-channel network using sided channel information, proposing bandit-based algorithms to minimize data freshness delay.

Contribution

It introduces age-dependent and age-independent algorithms within a linear contextual bandit framework for AoI optimization, demonstrating improved performance.

Findings

Age-dependent algorithm outperforms age-independent in simulations.

Proposed algorithms achieve sub-linear AoI regret.

Relationship established between AoI regret and channel selection times.

Abstract

We consider a discrete-time multi-channel network where the destination collects time-sensitive packets from multiple sources with sided channel information. The popular metric, Age of Information (AoI), is applied to measure the data freshness at the destination. Due to the interference constraint, only disjoint source-channel pairs can be chosen for transmission in each time slot, and the decision maker should choose the optimal scheduling pairs to minimize the average AoI at the destination. To learn the optimal channel selection, we apply the linear contextual bandit (LCB) framework by utilizing the sided information provided by pilots. Concretely, we establish the relationship between AoI regret and sub-optimal channel selection times and propose both age-independent and age-dependent algorithms. The former method is proven to achieve the sub-linear AoI regret but is outperformed by the latter algorithm both in the linear and non-linear contextual model in simulation.