Optimized Precoding for MU-MIMO With Fronthaul Quantization

TL;DR

This paper introduces a fronthaul-aware precoding method for MU-MIMO systems in 5G, optimizing performance under capacity constraints with a focus on practical, low-complexity solutions.

Contribution

It proposes a novel fronthaul-aware precoding design using sphere decoding and a heuristic approach for massive MIMO, improving sum rate performance under quantization constraints.

Findings

Proposed precoding outperforms previous methods in sum rate.

Heuristic approach achieves near-optimal performance with lower complexity.

Effective under both perfect and imperfect channel conditions.

Abstract

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information.