Efficient Design of Fronthaul-Constrained Uplink Reception for Cell-Free XL-MIMO

TL;DR

This paper proposes a scalable, fronthaul-efficient uplink reception design for cell-free XL-MIMO systems, optimizing linear transforms and compression to maximize sumrate with reduced complexity.

Contribution

It introduces a novel accelerated fractional programming algorithm with decentralized implementation for joint optimization in XL-MIMO uplink reception.

Findings

A-FP reduces computational complexity significantly.

The proposed scheme outperforms baseline methods relying on local CSI.

Fronthaul overhead remains independent of the number of AP antennas.

Abstract

With the evolution of multiple-input multiple-output (MIMO) technology toward extremely large (XL) MIMO systems comprising hundreds of, or more, antennas, this work investigates scalable and fronthaul-efficient reception design for the uplink of cell-free (CF) XL-MIMO systems. In such systems, the uplink signals transmitted by mobile user equipments (UEs) are jointly decoded at a central processing unit (CPU) connected to distributed access points (APs) via finite-capacity fronthaul links. We address the joint optimization of linear transform matrices, used by the APs to reduce the signal dimension and fronthaul load, and fronthaul compression strategies to maximize the uplink sumrate. A fractional programming (FP)-based iterative algorithm is first developed, followed by a reduced-complexity variant, termed accelerated FP (A-FP), along with its decentralized implementation whose fronthaul overhead remains independent of the number of AP antennas. Numerical results show that the proposed A-FP scheme significantly reduces computational complexity compared to FP implemented with general-purpose solvers, while substantially outperforming scalable baseline schemes that rely solely on local channel state information.