Max-min fairness energy efficiency optimization for cognitive networks based on unsupervised deep learning

Abstract

This paper aims to study the power control method for fairness energy efficiency (EE) improvement in cognitive radio networks (CRN) with interference channels, among one primary user (PU) shares the spectrum to multiple secondary users (SUs). The objective is to control the transmit power to maximize the minimum EE among all users subject to the quality of service (QoS) constraints. An extremely challenging non-convex max-min fraction optimization issue given due consideration. This work aims at developing an adaptive solving method based on deep learning (DL) techniques for the max-min EE optimization problem. To achieve such an objective, we construct a deep neural network (DNN), with the channel state information (CSI) being the input of DNN and the transmit power being the output of DNN. However, this faces two challenges. On the one hand, it is difficult to obtain label data. On the other hand, when DNNs are applied, it is very important to consider that QoS constraints should be met. These difficulties are circumvented in our work by designing an unsupervised learning strategy, in which a loss function is devised by combining the max-min EE objective and the QoS constraints via the barrier function method. The effectiveness of our proposed algorithm is ultimately demonstrated by the simulation results.