Age-Optimal Multi-Channel-Scheduling under Energy and Tolerance Constraints

TL;DR

This paper develops an optimal scheduling framework for multi-channel wireless updates under energy and age constraints, providing insights into threshold policies and proposing an online scheduler that adapts without channel knowledge.

Contribution

It formulates age-optimization as a constrained Markov Decision Process and derives the optimal scheduler via Linear Programming, offering new insights into energy allocation policies.

Findings

Optimal scheduler characterized for average-age and violation-rate minimization.

Online scheduler performs close to optimal without channel statistics.

Threshold-based policies are not suitable for this problem.

Abstract

We study the optimal scheduling problem where n source nodes attempt to transmit updates over L shared wireless on/off fading channels to optimize their age performance under energy and age-violation tolerance constraints. Specifically, we provide a generic formulation of age-optimization in the form of a constrained Markov Decision Processes (CMDP), and obtain the optimal scheduler as the solution of an associated Linear Programming problem. We investigate the characteristics of the optimal single-user multi-channel scheduler for the important special cases of average-age and violation-rate minimization. This leads to several key insights on the nature of the optimal allocation of the limited energy, where a usual threshold-based policy does not apply and will be useful in guiding scheduler designers. We then investigate the stability region of the optimal scheduler for the multi-user case. We also develop an online scheduler using Lyapunov-drift-minimization methods that do not require the knowledge of channel statistics. Our numerical studies compare the stability region of our online scheduler to the optimal scheduler to reveal that it performs closely with unknown channel statistics.