Spectrum Sensing for OFDMA Using Multicarrier Covariance Matrix Aware CNN  

作　　者：ZHANG Jintao HE Zhenqing RUI Hua XU Xiaojing 

机构地区：[1]National Key Laboratory of Science and Technology on Communications,University of Electronic Science and Technology of China,Chengdu 611731,China [2]ZTE Corporation,Shenzhen 518057,China [3]State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518055,China

出　　处：《ZTE Communications》2022年第3期61-69,共9页中兴通讯技术（英文版）

基　　金：supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.HC-CN-2020120002。

摘　　要：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 OFDM A scenario under the 5 G new radio network.

关 键 词：cognitive radio spectrum sensing OFDMA deep learning 5G new radio 

分 类 号：TN929.5[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]