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.

翻译：我们考虑一个5G蜂窝网络，其中gNB调度来自多个UE的时敏上行传输，并将接收到的数据包转发至远程目的地。在实际5G网络中，gNB无法直接观测到目的地侧的信息时效性（AoI），且需在严格的时隙级运行时约束下做出调度决策。本文针对有限可观测性场景，提出一种低复杂度的AoI感知调度策略。我们首先设计一种低复杂度估计器，通过gNB可观测数据推断UE侧数据包时间戳及目的地侧AoI。基于这些估计值，我们提出并在NetSim（一种具有标准兼容协议栈的5G模拟器）中实现了最大权重策略（MW-LC），以展示其相较于基线5G调度策略的性能优势。此外，我们利用MATLAB仿真表明，LC估计器与MW-LC的性能接近文献中基于更丰富估计器的AoI策略。该估计器可能对学术界具有独立参考价值，可支持5G调度之外的AoI感知算法。