Spectrum Sensing Method Based on Information Geometry and Deep Neural Network Due to the scarcity of radio spectrum resources and the growing demand, the use of spectrum sensing technology to improve the utilization of spectrum resources has become a hot research topic. In order to improve the utilization of spectrum resources, this paper proposes a spectrum sensing method that combines information geometry and deep learning. Firstly, the covariance matrix of the sensing signal is projected onto the statistical manifold. Each sensing signal can be regarded as a point on the manifold. Then, the geodesic distance between the signals is perceived as its statistical characteristics. Finally, deep neural network is used to classify the dataset composed of the geodesic distance. Simulation experiments show that the proposed spectrum sensing method based on deep neural network and information geometry has better performance in terms of sensing precision.

A TensorBased Spectrum Sensing Technique for MIMO Cognitive Radio Networks Despite the numerous spectrum sensing techniques, the existing techniques fail in providing an efficient spectrum sensing whenever a hidden terminal problem arises. Meanwhile, this problem can happen at any time in any severely fading primarytosecondary channels resulting in very low primary signaltonoise ratios (SNRs) and hence ineffective detection of the primary user in a cognitive radio (CR). Towards overcoming this problem, by introducing a tensorbased hypothesis testing framework, this paper proposes an efficient tensorbased detector (TBD) for a multipleinput multipleoutput (MIMO) CR networks over multipath fading channels. MonteCarlo simulations demonstrate that TBD outperforms the generalized likelihood ratio test (GLRT) detector and maximumminimum eigenvalue (MME) detector, especially in the very low SNR regime which is a manifestation of the hidden terminal problem.