# Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

arXiv: https://arxiv.org/abs/2107.02520 · HTML: https://mlsanity.com/paper/2107.02520/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Deep Learning Methods for Universal MISO Beamforming](https://mlsanity.com/paper/2007.00841/) (2020)
- [Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning](https://mlsanity.com/paper/2001.00784/) (2020)
- [Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration](https://mlsanity.com/paper/2002.12589/) (2020)
- [Cloud Radio Access Network: Virtualizing Wireless Access for Dense Heterogeneous Systems](https://mlsanity.com/paper/1512.07743/) (2015)
- [An Efficient Rate-Splitting Multiple Access Scheme for the Downlink of C-RAN Systems](https://mlsanity.com/paper/1907.02153/) (2019)

## Built on by (most cited first, 1 of 1)

- [Learning Decentralized Power Control in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2303.02573/) (2023)

## Abstract (arXiv)

Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.