Ensemble deep learning based resource allocation for multi-channel underlay cognitive radio system

Abstract

This paper proposes a resource allocation strategy for multi-channel underlay cognitive radio (CR) systems by means of an ensemble deep learning framework. The transmit power of secondary users (SUs) allocated to each channel is determined to maximize the overall spectral efficiency (SE), whilst meeting the interference constraint on the primary user (PU). To this end, a deep neural network (DNN) structure is developed, in which multiple DNN units are jointly utilized, to obtain the diversity over different DNNs. Our simulation results confirm that the proposed scheme can achieve near-optimal performance with a low computation time of less than 1.5 ms.