Spectrum Sensing for OFDMA Using Multicarrier Covariance Matrix Aware CNN

Abstract: We consider spectrum sensing problems in the orthogonal frequency division multiplexing access (OFDMA) cognitive radio scenario, where a secondary user with multiple antennas detects several consecutive subcarriers of an entire OFDM symbol occupied by multiple primary users. Specifically, an OFDM multicarrier covariance matrix convolutional neural network (CNN)-based approach is proposed for simultaneously detecting the occupancy of all OFDM subcarriers, where the multicarrier sample covariance matrix array is specially set as the input of
the CNN. The proposed approach can efficiently learn the energy information and correlation information between antennas and between subcarriers to significantly improve the spectrum sensing performance. Numerical results demonstrate that the proposed method has a substantial performance advantage over the state-of-the-art spectrum sensing methods in an OFDMA scenario under the 5G new radio network.
Keywords: cognitive radio; spectrum sensing; OFDMA; deep learning; 5G new radio