Power Control for Interference Management via Ensembling Deep Neural Networks

Abstract

A deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network specifically designed for the power control problem. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages a unsupervised learning strategy and directly maximizes the sum rate in the training phase. Observing that a single PCNet does not universally outperform the existing solutions, we further propose ePCNet, a network ensemble with multiple PCNets trained independently. Simulation results show that for the standard symmetric $K$-user Gaussian interference channel, the proposed methods can outperform all state-of-the-art power control solutions under various system configurations with a reduced computational complexity.