Imbalanced learning for cooperative spectrum sensing in cognitive radio networks

Document Type

Conference Proceeding

Date of Original Version



We propose a novel cooperative spectrum sensing (CSS) framework for cognitive radio networks based on imbalanced learning techniques, which aims to resolve the skewed category distribution problems of signal data. For a radio channel shared by primary users (PUs) and secondary users (SUs), the signal data composed of energy vectors, in which each energy level is estimated by SU, can be used to detect the channel availability via a classifier. However, due to the nature of this application, the existing category-imbalance problem hinders the detection performance since the trained classifier has a better effect on the dominated category. To enhance the performance, sampling (e.g., oversampling, under-sampling, and combination) algorithms are employed to balance the training data set based on the imbalance degree metric of imbalance-ratio. The balanced training set then can be used to train classifiers with initial parameters, and the validation set can be utilized to tune as well as evaluate the classifiers. In the testing phase, the actual desired performance on unseen signal data can be determined based on the testing set, i.e., whether the channel is available or not. The performance of each sampling algorithm is measured in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The simulation results demonstrate the effectiveness of our proposed framework compared to traditional CSS methods.

Publication Title, e.g., Journal

2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings