Multiband Joint Spectrum Sensing via Covariance Matrix-Aware Convolutional Neural Network
Jintao Zhang, Zhen-Qing He, Rui Hua, Xiaojing Xu
Abstract
This work investigates multiband spectrum sensing in a cognitive radio network where a multi-antenna secondary user attempts to detect several consecutive frequency bands occupied by multiple primary users. Specifically, we propose a multiband joint spectrum sensing approach based on the covariance matrix-aware convolutional neural network (CNN), in which the multiband sample covariance matrices are concatenated as the input of CNN. The proposed approach is free of model assumptions and the hidden correlation features between subbands can be learned to improve the spectrum sensing performance. Simulation results show that the proposed algorithm outperforms the state-of-the-art spectrum sensing methods in cases with and without the noise uncertainty.