Spectrum sensing is the major challenge in the cognitive radio (CR). We propose to learn local feature and use it as the prior knowledge to improve the detection performance. We define the local feature as the leading eigenvector derived from the received signal samples. A feature learning algorithm (FLA) is proposed to learn the feature blindly. Then, with local feature as the prior knowledge, we propose the feature template matching algorithm (FTM) for spectrum sensing. We use the discrete Karhunen--Lo{è}ve transform (DKLT) to show that such a feature is robust against noise and has maximum effective signal-to-noise ratio (SNR). Captured real-world data shows that the learned feature is very stable over time. It is almost unchanged in 25 seconds. Then, we test the detection performance of the FTM in very low SNR. Simulation results show that the FTM is about 2 dB better than the blind algorithms, and the FTM does not have the noise uncertainty problem.
Generative Spectrum Cartography: Unified Reconstruction and Active Sensing via Diffusion Models
Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI), Zhejiang University（浙江大学伊利诺伊大学厄巴纳香槟校区联合学院，浙江大学）；Tsinghua Space Center and Beijing National Research Center for Information Science and Technology, Tsinghua University（清华航天航空学院与北京信息科学与技术国家研究中心，清华大学）；Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（教育部无线通信重点实验室，北京邮电大学信息与通信工程学院）
