Optimizing Age of Information in Random Access Networks: A Second-Order Approach for Active/Passive Users

TL;DR

This paper introduces a second-order analytical approach to optimize Age of Information in random access networks with active and passive users, leading to improved transmission policies.

Contribution

It develops a second-order analysis framework using mean and variance of user behavior to optimize AoI, a novel approach in this context.

Findings

Optimized policies outperform baseline strategies in simulations.

AoI moments can be approximated by mean and variance of user transmission processes.

Second-order analysis provides a new tool for AoI optimization in complex networks.

Abstract

In this paper, we study the moments of the Age of Information (AoI) for both active and passive users in a random access network. In this network, active users broadcast sensing data, while passive users detect in-band radio activities from out-of-network devices, such as jammers. Collisions occur when multiple active users transmit simultaneously. Passive users can detect radio activities only when no active user transmits. Each active user's transmission behavior follows a Markov process. We aim to minimize the weighted sum of any moments of AoI for both user types. To achieve this, we employ a second-order analysis of system behavior. Specifically, we characterize an active user's transmission Markov process using its mean and temporal variance. We show that any moment of the AoI can be approximated by a function of these two parameters. This insight enables us to analyze and optimize the transmission Markov process for active users. We apply this strategy to two different random access models. Simulation results show that policies derived from this strategy outperform other baseline policies.