# Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems

arXiv: https://arxiv.org/abs/2306.11865 · HTML: https://mlsanity.com/paper/2306.11865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [A Graph Neural Network Approach for Scalable Wireless Power Control](https://mlsanity.com/paper/1907.08487/) (2019)
- [Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach](https://mlsanity.com/paper/1812.10044/) (2018)
- [Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air](https://mlsanity.com/paper/2207.08498/) (2022)
- [Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach](https://mlsanity.com/paper/2212.14051/) (2022)
- [Learning Deterministic Policy with Target for Power Control in Wireless Networks](https://mlsanity.com/paper/1902.07903/) (2019)

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

- [Learning Power Control Protocol for In-Factory 6G Subnetworks](https://mlsanity.com/paper/2505.05967/) (2025)

## Abstract (arXiv)

Transmit power control (TPC) is a key mechanism for managing interference, energy utilization, and connectivity in wireless systems. In this paper, we propose a simple low-complexity TPC algorithm based on the deep unfolding of the iterative projected gradient descent (PGD) algorithm into layers of a deep neural network and learning the step-size parameters. An unsupervised learning method with either online learning or offline pretraining is applied for optimizing the weights of the DNN. Performance evaluation in dense device-to-device (D2D) communication scenarios showed that the proposed method can achieve better performance than the iterative algorithm with more than a factor of 2 lower number of iterations.