# Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms

arXiv: https://arxiv.org/abs/2403.18930 · HTML: https://mlsanity.com/paper/2403.18930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 17 Feb 2024.

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

- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding](https://mlsanity.com/paper/2004.10715/) (2020)
- [Energy-Efficient Power Control: A Look at 5G Wireless Technologies](https://mlsanity.com/paper/1503.04609/) (2015)
- [Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels](https://mlsanity.com/paper/1908.11013/) (2019)
- [A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks](https://mlsanity.com/paper/2011.08970/) (2020)
- [Deep Unfolded Multicast Beamforming](https://mlsanity.com/paper/2004.09345/) (2020)

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

- [ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions](https://mlsanity.com/paper/2506.15837/) (2025)

## Abstract (arXiv)

In this work, we conduct a comparative study on two deep unfolding mechanisms to efficiently perform power control in the next generation wireless networks. The power control problem is formulated as energy efficiency over multiple interference links. The problem is nonconvex. We employ fractional programming transformation to design two solutions for the problem. The first solution is a numerical solution while the second solution is a closed-form solution. Based on the first solution, we design a semi-unfolding deep learning model where we combine the domain knowledge of the wireless communications and the recent advances in the data-driven deep learning. Moreover, on the highlights of the closed-form solution, fully deep unfolded deep learning model is designed in which we fully leveraged the expressive closed-form power control solution and deep learning advances. In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.