Joint AGC and Receiver Design for Large-Scale MU-MIMO Systems Using Low-Resolution Signal Processing in C-RANs

TL;DR

This paper introduces a joint optimization of automatic gain control and low-resolution aware MMSE filtering for large-scale MU-MIMO systems in C-RANs, significantly improving performance with low-resolution ADCs.

Contribution

It proposes a novel joint AGC and LRA-MMSE receiver design specifically tailored for low-resolution quantized MU-MIMO systems in C-RANs, enhancing system performance.

Findings

Substantial BER and rate improvements over existing methods.

Effective mitigation of quantization effects with joint optimization.

Analysis confirms increased sum rates and manageable complexity.

Abstract

Large-scale multi-user multiple-input multiple-output (MU-MIMO) systems and cloud radio access networks (C-RANs) are considered promising technologies for the fifth generation (5G) of wireless networks. In these technologies, the use of low-resolution analog-to-digital converters (ADCs) is key for energy efficiency and for complying with constrained fronthaul links. Processing signals with few bits implies a significant performance loss and, therefore, techniques that can compensate for quantization distortion are fundamental. In wireless systems, an automatic gain control (AGC) precedes the ADCs to adjust the input signal level in order to reduce the impact of quantization. In this work, we propose the joint optimization of the AGC, which works in the remote radio heads (RRHs), and a low-resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works in the cloud unit (CU), for large-scale MU-MIMO systems with coarsely quantized signals. We develop linear and successive interference cancellation (SIC) receivers based on the proposed joint AGC and LRA MMSE (AGC-LRA-MMSE) approach. An analysis of the achievable sum rates along with a computational complexity study is also carried out. Simulations show that the proposed AGC-LRA-MMSE design provides substantial gains in bit error rates and achievable information rates over existing techniques.