Cooperative Spectrum Sensing Based on Manifold Neural Networks
This study proposes an innovative collaborative spectrum sensing scheme based on Riemannian manifold neural networks, addressing the performance limitations of traditional spectrum sensing in complex signal environments, such as low signal-to-noise ratios and multipath fading. The method initially maps the signal matrices from multiple collaborative users onto a Riemanni-an manifold, generating covariance matrices with geometric characteristics. Subsequently, Riemannian manifold neural networks are utilized for efficient signal feature classification and spectrum sensing. The Riemannian manifold neural networks not only fully lever-age the advantages of Riemannian manifolds in non-Euclidean data structures, but also combine the powerful expressive capabilities of neural networks, thus demonstrating significantly superior spectrum sensing performance in various complex environments. A se-ries of detailed simulation experiments validate the superior performance of this method in diverse environments, showcasing its po-tential application value in actual wireless communication systems.
万方数据
维普
