Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul

TL;DR

This paper addresses the challenge of limited fronthaul capacity in uplink CRANs by leveraging massive MIMO technology, proposing an optimization algorithm to maximize sum-rate with different combining schemes.

Contribution

It introduces a joint optimization framework for precoders, quantization noise, and power in uplink CRANs with massive MIMO fronthaul, using an iterative MM-based algorithm.

Findings

Sum-rate approaches an asymptote with increasing RRH power.

MR scheme converges faster to its asymptote than ZF.

Proposed method effectively enhances uplink CRAN performance.

Abstract

The limited fronthaul capacity is known to be one of the main problems in cloud radio access networks (CRANs), especially in the wireless fronthaul links. In this paper, we consider the uplink of a CRAN system, where massive multiple-input multiple-output (MIMO) is utilized in the fronthaul link. Considering multi-antenna user equipment (UEs) and multi-antenna remote radio heads (RRHs), we maximize the system sum-rate by jointly optimizing the precoders at the UEs and the quantization noise covariance matrices and transmit powers at the RRHs. To solve the resulting nonconvex problem, an iterative algorithm based on the majorization-minimization (MM) method is proposed. Two schemes at the central unit are considered, namely maximum ratio (MR) and zero-forcing (ZF) combining. Numerical results show that the sum-rate has an asymptotic behaviour with respect to the maximum available power at RRHs and that the MR scheme goes to its asymptote faster than the ZF scheme.