In this work, we study the cross-layer timely throughput maximization for extended reality (XR) applications through uplink multi-user MIMO (MU-MIMO) scheduling. Timely scheduling opportunities are characterized by the peak age of information (PAoI)-metric and are incorporated into a network-side optimization problem as constraints modeling user satisfaction. The problem being NP-hard, we resort to a signaling-free, weighted proportional fair-based iterative heuristic algorithm, where the weights are derived with respect to the PAoI metric. Extensive numerical simulation results demonstrate that the proposed algorithm consistently outperforms existing baselines in terms of XR capacity without sacrificing the overall system throughput.

翻译：本文研究了通过上行链路多用户MIMO（MU-MIMO）调度实现扩展现实（XR）应用的跨层及时吞吐量最大化问题。及时调度机会由信息峰值年龄（PAoI）度量表征，并作为建模用户满意度的约束条件纳入网络侧优化问题。鉴于该问题属于NP难问题，我们提出一种免信令、基于加权比例公平的迭代启发式算法，其权重根据PAoI度量推导得出。大量数值仿真结果表明，所提算法在不牺牲整体系统吞吐量的前提下，在XR容量方面持续优于现有基线方法。