# Joint Quantized Precoding and Bit Allocation for Fronthaul-Constrained Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/2609.26149 · HTML: https://mlsanity.com/paper/2609.26149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 12 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Foundations of User-Centric Cell-Free Massive MIMO](https://mlsanity.com/paper/2108.02541/) (2021)
- [Optimized Precoding for MU-MIMO With Fronthaul Quantization](https://mlsanity.com/paper/2209.01868/) (2022)
- [Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems](https://mlsanity.com/paper/2403.19132/) (2024)
- [Quantized Precoding for Maximizing Sum Rate in MU-MIMO Systems with Constrained Fronthaul](https://mlsanity.com/paper/2602.23856/) (2026)
- [Stream-Adaptive Quantization and Power Allocation in Fronthaul-Constrained MIMO Systems](https://mlsanity.com/paper/2604.11471/) (2026)

## Abstract (arXiv)

We study quantization-aware precoding for the downlink of cell-free massive MIMO systems with limited-resolution fronthaul. In such systems, the centrally designed precoder must be quantized before being conveyed to distributed access points (APs), creating a strong coupling between precoder design and fronthaul compression. To capture this effect, we develop an end-to-end signal model based on Bussgang decomposition, where the quantization distortion depends explicitly on the precoder coefficients. Building on this model, we formulate a joint precoding and bit allocation problem under per-AP power constraints and a total fronthaul bit budget. We propose an efficient block coordinate descent algorithm that iteratively updates the precoder, receive scaling, and distortion levels. Numerical results demonstrate that the proposed method significantly outperforms uniform bit allocation and quantization-unaware precoding schemes, particularly in fronthaul-limited regimes.