# Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/2409.06715 · HTML: https://mlsanity.com/paper/2409.06715/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Generalization Bounds: Perspectives from Information Theory and PAC-Bayes](https://mlsanity.com/paper/2309.04381/) (2023)
- [Modular meta-learning](https://mlsanity.com/paper/1806.10166/) (2018)
- [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022)
- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Mnemosyne: Learning to Train Transformers with Transformers](https://mlsanity.com/paper/2302.01128/) (2023)
- [Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources](https://mlsanity.com/paper/2301.06166/) (2023)
- [Cloud-RAN functional split for an efficient fronthaul network](https://mlsanity.com/paper/2101.04216/) (2021)
- [Optimized Precoding for MU-MIMO With Fronthaul Quantization](https://mlsanity.com/paper/2209.01868/) (2022)
- [Quantisation-aware Precoding for MU-MIMO with Limited-capacity Fronthaul](https://mlsanity.com/paper/2202.08925/) (2022)
- [Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs](https://mlsanity.com/paper/2205.07518/) (2022)
- [Graph-based Framework for Flexible Baseband Function Splitting and Placement in C-RAN](https://mlsanity.com/paper/1501.04703/) (2015)
- [Weighted Sum Rate Maximization of the mmWave Cell-Free MIMO Downlink Relying on Hybrid Precoding](https://mlsanity.com/paper/2201.04332/) (2022)
- [Meta-Learning-Based Fronthaul Compression for Cloud Radio Access Networks](https://mlsanity.com/paper/2403.09004/) (2024)
- [Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN](https://mlsanity.com/paper/2311.03899/) (2023)

## Abstract (arXiv)

The conventional approach to the fronthaul design for cell-free massive MIMO system follows the compress-and-precode (CP) paradigm. Accordingly, encoded bits and precoding coefficients are shared by the distributed unit (DU) on the fronthaul links, and precoding takes place at the radio units (RUs). Previous theoretical work has shown that CP can be potentially improved by a significant margin by precode-and-compress (PC) methods, in which all baseband processing is carried out at the DU, which compresses the precoded signals for transmission on the fronthaul links. The theoretical performance gain of PC methods are particularly pronounced when the DU implements multivariate quantization (MQ), applying joint quantization across the signals for all the RUs. However, existing solutions for MQ are characterized by a computational complexity that grows exponentially with the sum-fronthaul capacity from the DU to all RUs. This work sets out to design scalable MQ strategies for PC-based cell-free massive MIMO systems. For the low-fronthaul capacity regime, we present alpha-parallel MQ (alpha-PMQ), whose complexity is exponential only in the fronthaul capacity towards an individual RU, while performing close to full MQ. alpha-PMQ tailors MQ to the topology of the network by allowing for parallel local quantization steps for RUs that do not interfere too much with each other. For the high-fronthaul capacity regime, we then introduce neural MQ, which replaces the exhaustive search in MQ with gradient-based updates for a neural-network-based decoder, attaining a complexity that grows linearly with the sum-fronthaul capacity. Numerical results demonstrate that the proposed scalable MQ strategies outperform CP for both the low and high-fronthaul capacity regimes at the cost of increased computational complexity at the DU (but not at the RUs).