Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems
This addresses interference and energy management in dense D2D wireless communications, representing an incremental improvement.
The paper tackled transmit power control in wireless systems by proposing an unsupervised deep unfolded PGD algorithm, which achieved better performance than the iterative algorithm with more than a factor of 2 lower number of iterations.
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.