Local Cooperative Sensing in 3D Spectrum Availability-Heterogeneous AAV Networks
Jiawen Li, Yonghua Wang, Yanqing Chen, Guanghai Xu, Bingfeng Zheng · IEEE Transactions on Vehicular Technology · 2024
The widespread application of autonomous aerial vehicles(AAVs) has put forward new demands on spectrum resources. In the more realistic spectrum availability-heterogeneous environments, where secondary users (SUs) may suffer from typically varied spectrum availability across different geographical areas, the effectiveness of the global cooperative spectrum sensing (SS) diminishes. To address this challenge, this paper investigates local cooperative sensing in 3D spectrum availability-heterogeneous cognitive radio networks (CRNs), with AAVs acting as SUs. A local cooperative sensing framework is proposed to ascertain spectrum status in this paper. Specifically, this framework utilizes the energy attenuation characteristics of the primary signal alongside a clustering algorithm to estimate primary user (PU) location, thereby facilitating the consistent spectrum availability for cooperative SUs and reducing the impacts of chaotic pathloss distribution. Then an information geometry (IG)-based data fusion method is incorporated to enable effective data processing on manifolds. Moreover, by drawing inspiration from deep learning (DL), a novel convolutional neural network (CNN) classifier based on the coordinate attention (CA) mechanism is proposed to classify the fused samples, thus achieving the spectrum decision. Numerical simulations demonstrate the superiority of the proposed scheme.