Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach
Rahil Sarikhani, Farshid Keynia
Abstract
Cognitive radio network (CRN) emerged to utilize the frequency bands efficiently. To use the frequency bands efficiently without any interference on the licensed user, detection of the frequency holes is the first step, which is called spectrum sensing in the context. In order to increase the quality of local spectrum sensing results, cooperative spectrum sensing (CSS) is introduced in the literature to combine the local sensing results. Recently, machine learning techniques are designed to improve the classification of the images and signals. Specifically, Deep Reinforcement Learning (DRL) is of interest for its substantial improvement in the classification problems. In this letter, we have proposed DRL based CSS algorithm, which is employed to decrease the signaling in the network of SUs. The simulation results represent the superiority of the proposed approach to state-of-the-art approaches, including Deep Cooperative Sensing (DCS), K-out-of-N, and Support Vector Machine (SVM) based CSS algorithms.