Toward Practical Age-of-Information Scheduling in 5G Cellular

TL;DR

This paper introduces a low-complexity, AoI-aware scheduling policy for 5G networks that operates under limited observability and real-time constraints, improving upon baseline policies.

Contribution

It develops a novel estimator and a Max-Weight scheduling policy tailored for practical 5G environments with limited AoI information.

Findings

The proposed estimator accurately infers destination AoI from gNB observations.

MW-LC policy achieves performance close to more complex AoI policies.

The estimator can be useful for other AoI-aware algorithms beyond 5G.

Abstract

We consider a 5G cellular network where a gNB schedules time-sensitive uplink transmissions from multiple UEs and forwards received packets to remote destinations. In practical 5G networks, the gNB does not directly observe the destination-side Age of Information (AoI) and must make scheduling decisions under stringent slot-level runtime constraints. In this paper, we develop a low-complexity AoI-aware scheduling policy for 5G cellular under limited observability. We first design a low-complexity estimator that infers UE-side packet timestamps and destination-side AoI from gNB-visible observations. Based on these estimates, we propose and implement a Max-Weight policy (MW-LC) in NetSim, a 5G emulator with a standards-compatible protocol stack, to showcase its performance against baseline 5G scheduling policies. Furthermore, we use MATLAB simulations to show that the LC estimator and MW-LC achieve performance close to a richer estimator-based AoI policy from the literature. The estimator may be of independent interest to the community, enabling AoI-aware algorithms beyond 5G scheduling.