Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources

TL;DR

This paper investigates energy-efficient deployment of cell-free massive MIMO within O-RAN, proposing joint resource orchestration across cloud, fronthaul, and radio to minimize power consumption while maintaining performance.

Contribution

It introduces an end-to-end resource orchestration scheme for cell-free massive MIMO in O-RAN, optimizing power across radio, fronthaul, and cloud resources, outperforming other schemes.

Findings

End-to-end orchestration reduces power consumption significantly.

Fully virtualized fronthaul and cloud resources enhance energy efficiency.

Algorithms effectively balance spectral efficiency and power minimization.

Abstract

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.