Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems

Ramoni Adeogun
6/20/2023
cs.LGeess.SP

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Abstract

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.

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Cite this paper

@article{adeogun2023unsupervised,
  title  = {Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems},
  author = {Ramoni Adeogun},
  year   = {2023},
  eprint = {2306.11865},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.11865v1}
}

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