# Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks

arXiv: https://arxiv.org/abs/2103.11284 · HTML: https://mlsanity.com/paper/2103.11284/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Estimating or Propagating Gradients Through Stochastic Neurons](https://mlsanity.com/paper/1305.2982/) (2013)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)
- [Techniques for Learning Binary Stochastic Feedforward Neural Networks](https://mlsanity.com/paper/1406.2989/) (2014)
- [Deep Learning for Distributed Optimization: Applications to Wireless Resource Management](https://mlsanity.com/paper/1905.13378/) (2019)
- [Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet](https://mlsanity.com/paper/2001.10199/) (2020)
- [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 Framework for Wireless Systems: Applications to Optical Wireless Communications](https://mlsanity.com/paper/1812.05227/) (2018)
- [A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver](https://mlsanity.com/paper/1910.12048/) (2019)
- [Cache Placement in Fog-RANs: From Centralized to Distributed Algorithms](https://mlsanity.com/paper/1710.00784/) (2017)

## Built on by (most cited first, 3 of 3)

- [A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization](https://mlsanity.com/paper/2207.05364/) (2022)
- [Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul](https://mlsanity.com/paper/2312.01288/) (2023)
- [Learning Decentralized Power Control in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2303.02573/) (2023)

## Abstract (arXiv)

Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures. The F-RAN entails a joint optimization of cloud and edge computing as well as fronthaul interactions, which is challenging for traditional optimization techniques. This paper proposes a Cloud-Enabled Cooperation-Inspired Learning (CECIL) framework, a structural deep learning mechanism for handling a generic F-RAN optimization problem. The proposed solution mimics cloud-aided cooperative optimization policies by including centralized computing at the cloud, distributed decision at the ENs, and their uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs) are employed for characterizing computations of the cloud and ENs. The forwardpass of the DNNs is carefully designed such that the impacts of the practical fronthaul links, such as channel noise and signling overheads, can be included in a training step. As a result, operations of the cloud and ENs can be jointly trained in an end-to-end manner, whereas their real-time inferences are carried out in a decentralized manner by means of the fronthaul coordination. To facilitate fronthaul cooperation among multiple ENs, the optimal fronthaul multiple access schemes are designed. Training algorithms robust to practical fronthaul impairments are also presented. Numerical results validate the effectiveness of the proposed approaches.