MU-MIMO Uplink Timely Throughput Maximization for Extended Reality Applications

TL;DR

This work addresses timely uplink throughput for XR in MU-MIMO cellular networks by using peak AoI ($PAoI$) as a cross-layer scheduling metric. It formulates a challenging NP-hard problem $P1$ that combines throughput, fairness via an $\alpha$-fair utility, and per-user $PAoI$ constraints, and then delivers a signaling-free, per-iteration heuristic that weights users according to $PAoI$ urgency. The proposed method, based on a weighted proportional-fair framework with PAoI-derived weights, demonstrates superior capacity and improved edge-user performance without sacrificing overall throughput in extensive simulations. The results highlight the practical potential of PAoI-aware, signaling-free scheduling to meet XR QoS requirements with reduced signaling overhead in dense uplink MU-MIMO deployments.

Abstract

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.

Figures (4)

• Figure 1: Goodput comparison of top $95\%$
• Figure 2: Goodput comparison of bottom $5\%$ and $10\%$
• Figure 3: distribution comparison
• Figure 4: Empirical distribution of co-scheduled