Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO

TL;DR

This paper addresses the fronthaul bottleneck in scalable, user-centric cell-free massive MIMO by jointly optimizing cluster processor placement and routed fronthaul traffic, while exploiting rate-distortion theory to adapt UL fronthaul quantization via a distortion parameter $D$. The authors formulate a mixed-integer linear program (MILP) to minimize a weighted sum of fronthaul link loads, subject to router–RU–DU flow constraints and cluster-placement capacities, and they analyze UL and DL data-rate driven fronthaul requirements with a practical UP/DL split (7.2x and 7.3) in an O-RAN-inspired architecture. Numerical results show that, with optimized $D$, fronthaul load remains stable over a wide range of active user counts $K$ and PHY SE is minimally degraded, highlighting the resilience of the cell-free MIMO PHY and fronthaul network to varying user densities; more importantly, the results demonstrate significant fronthaul savings as $D$ increases, while allowing scheduling to adapt to demand without overwhelming the fronthaul. The study illustrates that dense cell-free deployments can sustain high performance across fluctuating user loads, enabling resilient 6G RAN and fronthaul design with flexible user loads and routing strategies.

Abstract

We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric cell-free massive MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the radio units in the cluster must exchange data with the corresponding DU over the fronthaul. Because the fronthaul links have limited capacity, this data must be quantized before it is sent over the fronthaul. We consider a routed fronthaul network, where the cluster processor placement and fronthaul traffic routing are jointly optimized with a mixed-integer linear program. For different numbers of users in the network, we investigate the effect of fronthaul quantization rates, a system parameter computed based on rate-distortion theory. Our results show that with optimized quantization rates, the fronthaul load is quite stable for a wide range of user loads without significant PHY performance loss. This demonstrates that the cell-free massive MIMO PHY and fronthaul network are resilient to varying user densities.

Figures (3)

• Figure 1: An example of user-centric clusters, UE-RU association and the data exchange over the fronthaul for user $k$ and its cluster ${\cal C}_k$.
• Figure 2: Sum UL/DL fronthaul load and SE vs. $D$.
• Figure 3: 5th percentile user SE in the DL (left) and UL (right, same legend as for the UL applies) vs. $D$.