🤖 AI Summary
This work addresses the challenge in 5G cellular networks where base stations cannot directly observe the Age of Information (AoI) at user equipment while adhering to stringent slot-level scheduling constraints. To overcome this limitation, the authors propose a low-complexity AoI estimation algorithm that infers user-side timestamps and destination AoI using only base station–observable information. Building upon this estimator, they design a lightweight maximum-weight scheduling policy (MW-LC). The approach is the first to implement AoI-aware scheduling within a standards-compliant 5G simulation environment (NetSim). Experimental validation in MATLAB demonstrates that MW-LC achieves performance closely approaching that of an ideal scheduler with full AoI knowledge and significantly outperforms conventional 5G baseline schedulers.
📝 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.