# Optimized Precoding for MU-MIMO With Fronthaul Quantization

arXiv: https://arxiv.org/abs/2209.01868 · HTML: https://mlsanity.com/paper/2209.01868/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Fronthaul Quantization-Aware MU-MIMO Precoding for Sum Rate Maximization](https://mlsanity.com/paper/2406.19183/) (2024)
- [Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO](https://mlsanity.com/paper/2409.06715/) (2024)
- [AI-Driven Fronthaul Link Compression in Wireless Communication Systems: Review and Method Design](https://mlsanity.com/paper/2509.04805/) (2025)
- [Joint Quantized Precoding and Bit Allocation for Fronthaul-Constrained Cell-Free Massive MIMO](https://mlsanity.com/paper/2609.26149/) (2026)

## Abstract (arXiv)

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.