Age-Gain-Dependent Random Access for Event-Driven Periodic Updating

TL;DR

This paper introduces an age-gain-dependent random access scheme for event-driven periodic updates, optimizing network age of information through decentralized parameter adjustment based on age gain knowledge.

Contribution

It presents a novel analytical modeling approach and decentralized algorithms for age gain-aware access control in random access networks, improving AoI performance.

Findings

The proposed schemes outperform existing methods in various network scenarios.

Analytical models accurately predict AoI improvements.

Decentralized parameter tuning effectively reduces network age of information.

Abstract

This paper considers utilizing the knowledge of age gains to reduce the network average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easily implementable decentralized manner, so that the unavoided contention can be limited to devices with age gains as high as possible. For the basic case that each device utilizes its knowledge of age gain of only itself, we provide an analytical modeling approach by a multi-layer discrete-time Markov chains (DTMCs), where an external infinite-horizon DTMC manages the jumps between the beginnings of frames and an internal finite-horizon DTMC manages the evolution during an arbitrary frame. Such modelling enables that optimal access parameters can be obtained offline. For the enhanced case that each device utilizes its knowledge of age gains of all the devices, we require each device to adjust its access parameters for maximizing the estimated network \textit{expected AoI reduction} (EAR) per slot, which captures the essential for improving the contribution of the throughput to the AoI performance. To estimate the network EAR, we require each device to use Bayes' rule to keep a posteriori joint probability distribution of local age and age gain of an arbitrary device based on the channel observations. Numerical results validate our theoretical analysis and demonstrate the advantage of the proposed schemes over the existing schemes in a wide range of network configurations.