Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless
  Systems
Ramoni Adeogun
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.