Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems

• URL: http://arxiv.org/abs/2306.11865v1
• Date: Tue, 20 Jun 2023 19:51:21 GMT
• Title: Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems
• Authors: Ramoni Adeogun
• Abstract summary: We propose a simple low-complexity TPC algorithm based on the deep unfolding of the iterative projected gradient (PGD) algorithm into layers of a deep neural network and learning the step-size parameters. 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.
• Score: 0.6091702876917281
• License: http://creativecommons.org/licenses/by/4.0/
• 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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