Distributed User Scheduling in Multi-Cell MIMO O-RAN with QoS Constraints

TL;DR

This paper proposes a distributed user scheduling algorithm for multi-cell MIMO O-RAN that maximizes throughput while satisfying QoS constraints, using a novel rate approximation and lightweight coordination.

Contribution

It introduces a distributed scheduling solution that closely matches centralized performance with minimal coordination, addressing the complexity of multi-user MIMO O-RAN systems.

Findings

Distributed scheduler achieves near-centralized performance.

The scheme ensures high scalability and fast convergence.

It effectively satisfies user QoS requirements in large networks.

Abstract

Distributed scheduling is essential for open radio access network (O-RAN) employing advanced physical-layer techniques such as multi-user MIMO (MU-MIMO), carrier aggregation (CA), and joint transmission (JT). This work investigates the multi-component-carrier (multi-CC) resource block group (RBG) scheduling in MU-MIMO O-RAN with both JT and non-JT users. We formulate a scheduling optimization problem to maximize throughput subject to user-specific quality of service (QoS) requirements while ensuring consistent allocations across cooperating O-RAN radio units (O-RUs) required by JT transmission. The strong variable coupling, non-convexity, and combinatorial complexity make the problem highly challenging. To tackle this, we extend the eigen-based zero-forcing transceiver design to JT users and leverage massive MIMO asymptotic properties to derive a tractable, separable rate approximation. Building on this, we develop two solutions: a centralized block coordinate descent benchmark and a distributed scheduler aligned with the O-RAN architecture. The proposed distributed scheme achieves near-centralized performance with only one round of lightweight coordination among cells, significantly reducing complexity and delay. Extensive simulations validate that our distributed scheduler achieves high scalability, fast convergence, and better QoS satisfaction rate in large-scale MU-MIMO networks.