Learning Power Allocation for Cellular Systems with Data Rate-based Deep Neural Network

Abstract

Optimizing power allocation in cellular systems with deep learning enables real-time coordination of inter-cell interference. When channels are time-varying, the deep neural networks (DNNs) need to be re-trained frequently and the training samples need to be re-collected in a timely manner. To achieve higher sum rate with fewer training samples and lower training cost, domain knowledge should be resorted for designing DNNs. In this paper, we propose a DNN structure where the formula of data rate is used to facilitate the learning of power allocation policy, called data-rate based DNN (DRNN). Since such a model-based deep learning method does not exclude the use of prior knowledge for reducing the hypothesis space of a DNN, we further exploit a permutation equivariance prior by introducing parameter sharing into the DNN structure. By integrating the model and prior into DNN, simulations show that either sum rate is improved for given number of training samples or training complexity is reduced to achieve an expected performance.