Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems
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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.
Key facts. Cited by 1 paper 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.
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6 of 16 references.
References · 6
- 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
- 2019A Graph Neural Network Approach for Scalable Wireless Power Controlcited by 26
- 2018Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approachcited by 7
- 2022Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Aircited by 7
- 2022Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approachcited by 2
- 2019Learning Deterministic Policy with Target for Power Control in Wireless Networkscited by 1
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1 paper.