MU-MIMO Uplink Timely Throughput Maximization for Extended Reality Applications

TL;DR

This paper proposes a novel heuristic algorithm for uplink MU-MIMO scheduling that maximizes timely throughput for XR applications by considering peak age of information, significantly improving capacity without reducing overall throughput.

Contribution

It introduces a signaling-free, PAoI-aware scheduling heuristic for uplink MU-MIMO, addressing the NP-hard problem for XR applications.

Findings

The proposed algorithm outperforms existing baselines in XR capacity.

It maintains system throughput while enhancing timely data delivery.

The approach effectively incorporates PAoI into scheduling decisions.

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.