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.
@article{arxiv.2306.11865,
title = {Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems},
author = {Ramoni Adeogun},
journal= {arXiv preprint arXiv:2306.11865},
year = {2023}
}