Energy-Efficient Power Control in Wireless Networks With Spatial Deep Neural Networks

Abstract

The energy-efficient power control of interfering links in a large wireless network is a challenging task. In this paper, we propose a deep learning based power control scheme, termed PowerNet, that uses the devices' geographical location information (GLI). We show that it is possible to bypass the complex channel estimation process and directly perform power control with GLI when the channel state information (CSI) can be viewed as a function of distance dependent path-loss. The time consuming and complex channel estimation process can thus be avoided. Moreover, with a proper training, PowerNet transforms the on-line complexity to off-line training, and is amenable for real-time services. Different from conventional deep neural network (DNN) that adopts fully connected structure, the proposed PowerNet leverages convolutional layers to better capture the interference pattern across different links in large wireless networks and utilizes deep residual learning to further enhance its robustness. Simulation results demonstrate that PowerNet can achieve a near-optimal performance at a remarkably high speed without explicit channel estimation. PowerNet also exhibits a great generalization ability in terms of problem sizes and channel fading types.