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

TL;DR

The paper addresses energy-efficient deployment of cell-free massive MIMO in O-RAN by jointly allocating radio, optical fronthaul, and cloud processing resources across end-to-end paths. It develops two end-to-end optimization problems: one for minimizing total end-to-end power under SINR/QoS constraints, and another that jointly maximizes sum SE while minimizing power, solved via mixed binary second-order cone programming and a concave-convex programming framework with l0-norm approximations. The authors compare fully virtualized end-to-end orchestration with local cloud coordination and radio-only schemes across two fronthaul functional splits, showing substantial power savings (up to 39%) and rate gains (up to 1.7x) for CF-MIMO in O-RAN, particularly with FS-8; results also reveal SE improvements for the worst-case users. The work demonstrates practical energy-efficient network design insights for next-generation open and virtualized RANs, guiding deployment of CF-MIMO in O-RAN with end-to-end resource pooling and JT coordination.

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.

Figures (10)

• Figure 1: O-RAN architecture for cell-free massive MIMO with functional splitting options 8 and 7.2 in uplink and downlink.
• Figure 2: In each coherence block of $\tau_c= N_{\rm smooth}N_{\rm slot}$ complex samples, the channel is modeled as time-invariant and frequency-flat according to the block-fading model. Out of total number of $N_{\rm DFT}$ subcarriers, $N_{\rm used}\leq N_{\rm DFT}$ subcarriers are utilized.
• Figure 3: The total power versus the SE requirement per UE for $L=16$ and $K=8$.
• Figure 4: Power consumption breakdown for the SE requirement of $1.25$ bit/s/Hz, $L=16$, and $K=8$.
• Figure 5: The total power versus the number of UEs for $L=36$ and the SE requirement of 2 bit/s/Hz.

Theorems & Definitions (2)

• Lemma 1
• proof