Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources
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Abstract (arXiv)
For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: i) "radio-only" where radio resources are optimized independently from the cloud and ii) "local cloud coordination" where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2023.
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Citing papers per year: 2024: 3; 2025: 0; 2026: 2.
5 papers.
- 2024Cell-Free Multi-User MIMO Equalization via In-Context Learningcited by 8
- 2024Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Managementcited by 3
- 2026EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RANcited by 1
- 2024Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMOcited by 0
- 2026Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approachcited by 0
- 2026Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach8 Apr 2026
- 2026EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN9 Feb 2026
- 2024Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management5 Dec 2024
- 2024Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO26 Aug 2024
- 2024Cell-Free Multi-User MIMO Equalization via In-Context Learning8 Apr 2024