# Fronthaul Quantization-Aware MU-MIMO Precoding for Sum Rate Maximization

arXiv: https://arxiv.org/abs/2406.19183 · HTML: https://mlsanity.com/paper/2406.19183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [Optimized Precoding for MU-MIMO With Fronthaul Quantization](https://mlsanity.com/paper/2209.01868/) (2022)
- [Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?](https://mlsanity.com/paper/2205.06225/) (2022)

## Abstract (arXiv)

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the precoding matrix is selected in a baseband unit (BBU) and then sent over a digital fronthaul to the transmitting antenna array. The fronthaul has a limited bit resolution with a known quantization behavior. We formulate a new sum rate maximization problem where the precoding matrix elements must comply with the quantizer. We solve this non-convex mixed-integer problem to local optimality by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. The precoding optimization subproblem becomes an integer least-squares problem, which we solve with a new algorithm using a sphere decoding (SD) approach. We show numerically that the proposed precoding technique vastly outperforms the baseline of optimizing an infinite-resolution precoder and then quantizing it. We also develop a heuristic quantization-aware precoding that outperforms the baseline while having comparable complexity.