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

Manobendu Sarker, Md. Zoheb Hassan, Xianbin Wang

TL;DR

This paper proposes a QoS-driven and multi-connectivity-enabled association algorithm incorporating UAV-centric and O-RU-centric criteria with targeted refinement for weak UAVs, and a bisection-guided fixed-point power control algorithm achieving global optimality with significantly reduced complexity.

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.

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

TL;DR

This paper proposes a QoS-driven and multi-connectivity-enabled association algorithm incorporating UAV-centric and O-RU-centric criteria with targeted refinement for weak UAVs, and a bisection-guided fixed-point power control algorithm achieving global optimality with significantly reduced complexity.

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.
Paper Structure (22 sections, 6 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 22 sections, 6 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Large-Scale Fading
  5. Small-Scale Fading
  6. Channel Estimation
  7. Uplink Data Transmission and Spectral Efficiency
  8. Problem Formulation
  9. Proposed Solution
  10. UAV-O-RU Association Assignment
  11. Proposed UAV-O-RU Association Assignment Scheme
  12. Computational Complexity Analysis
  13. UL Data Transmit Power Allocation
  14. Proposed BG-FPPC Algorithm
  15. Computational Complexity
  16. ...and 7 more sections

Figures (5)

  • Figure 1: O-RAN-enabled CF mMIMO system for 5G ariel corridor.
  • Figure 2: Average minimum SE performance of different schemes for varying UAV $K$ with $\mathrm{SE}^{\min}=1~\text{bit/s/Hz}$.
  • Figure 3: Average success rate performance of different schemes for varying UAV $K$ with $\mathrm{SE}^{\min}=1~\text{bit/s/Hz}$.
  • Figure 4: Average fairness performance of different schemes for varying UAV $K$ with $\mathrm{SE}^{\min}=1~\text{bit/s/Hz}$.
  • Figure 5: Average runtime of different schemes for varying UAV $K$ with $\mathrm{SE}^{\min}=1~\text{bit/s/Hz}$.