Closed-loop Uplink Radio Resource Management in CF-O-RAN Empowered 5G Aerial Corridor

TL;DR

This paper presents a novel closed-loop uplink radio resource management framework for 5G aerial corridors using CF-O-RAN, optimizing spectral efficiency and QoS with efficient algorithms and environment-aware CSI inference.

Contribution

It introduces a joint optimization approach for UAV association and power control in CF-O-RAN, with algorithms that are computationally efficient and suitable for real-time deployment.

Findings

Up to 440% improvement in minimum spectral efficiency.

Achieved 100% QoS satisfaction and fairness.

Reduced runtime by up to 99.7% compared to traditional solvers.

Abstract

In this paper, we investigate the uplink (UL) radio resource management for 5G aerial corridors with an open-radio access network (O-RAN)-enabled cell-free (CF) massive multiple-input multiple-output (mMIMO) system. Our objective is to maximize the minimum spectral efficiency (SE) by jointly optimizing unmanned aerial vehicle (UAV)-open radio unit (O-RU) association and UL transmit power under quality-of-service (QoS) constraints. Owing to its NP-hard nature, the formulated problem is decomposed into two tractable sub-problems solved via alternating optimization (AO) using two computationally efficient algorithms. We then propose (i) a QoS-driven and multi-connectivity-enabled association algorithm incorporating UAV-centric and O-RU-centric criteria with targeted refinement for weak UAVs, and (ii) a bisection-guided fixed-point power control algorithm achieving global optimality with significantly reduced complexity, hosted as xApp at the near-real-time (near-RT) RAN intelligent controller (RIC) of O-RAN. Solving the resource-allocation problem requires global channel state information (CSI), which incurs substantial measurement and signaling overhead. To mitigate this, we leverage a channel knowledge map (CKM) within the O-RAN non-RT RIC to enable efficient environment-aware CSI inference. Simulation results show that the proposed framework achieves up to 440% improvement in minimum SE, 100% QoS satisfaction and fairness, while reducing runtime by up to 99.7% compared to an interior point solver-based power allocation solution, thereby enabling O-RAN compliant real-time deployment.