D2D Power Control Based on Hierarchical Extreme Learning Machine

Abstract

The interference in Device-to-Device (D2D) communications system is a major challenge. To cope with the severe interference, interference management techniques such as power control are needed. Because of the ability to learn automatically from the environment, the Q-learning algorithm has already been used as the D2D power control technique in many previous studies. But the algorithm is very time-consuming because of its multiple iterations. In this paper, a D2D power control method based on Hierarchical Extreme Learning Machine (H-ELM) is proposed. H-ELM is an effective supervised learning algorithm evolved from original Extreme Learning Machine (ELM). By comparing with the other two power control algorithms based on machine learning, distributed Q-learning and CART Decision Tree, the simulation results show that the method in this paper has a better performance in both communication throughput and energy efficiency with limited time consumption.