Scheduling Policies for AoI Minimization with Timely Throughput Constraints

TL;DR

This paper proposes a novel scheduling policy for wireless networks that minimizes Age of Information (AoI) while satisfying timely throughput constraints, using a Markov Decision Process framework and Lyapunov optimization.

Contribution

It introduces a new optimization formulation for AoI minimization with throughput constraints and develops a low-complexity algorithm with optimality guarantees.

Findings

The proposed algorithms satisfy throughput constraints while minimizing AoI.

Simulation results demonstrate convergence and effectiveness of the scheduling policies.

Trade-offs between AoI and throughput are analyzed and validated.

Abstract

In 5G and beyond communication systems, the notion of latency gets great momentum in wireless connectivity as a metric for serving real-time communications requirements. However, in many applications, research has pointed out that latency could be inefficient to handle applications with data freshness requirements. Recently, Age of Information (AoI) metric, which can capture the freshness of the data, has attracted a lot of attention. In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The formulated problem is cast as a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing the Lyapunov optimization theory and it can be proved that it is solved per frame by applying backward dynamic programming algorithms with optimality guarantees. In addition, we provide a low-complexity algorithm guaranteeing that the timely-throughput constraint is satisfied. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Simulation results show the convergence of the algorithms for different values of the weighted factor and the trade-off between the AoI and the timely throughput.