Abstract
In this paper, we introduce a sub-Nyquist sampling-based receiver architecture and method for wideband spectrum sensing. Instead of recovering the original wideband analog signal, the proposed method aims to directly reconstruct the power spectrum of the wideband analog signal from sub-Nyquist samples. Note that power spectrum alone is sufficient for wideband spectrum sensing. Since only the covariance matrix of the wideband signal is needed, the proposed method, unlike compressed sensing-based methods, does not need to impose any sparsity requirement on the frequency domain. The proposed method is based on a multi-coset sampling architecture. By exploiting the inherent sampling structure, a fast compressed power spectrum estimation method whose primary computational task consists of fast Fourier transform (FFT) is proposed. Simulation results are presented to show the effectiveness of the proposed method.
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Acknowledgements
We would like to express our deepest gratitude to the GBSense competition organizers for their tremendous efforts in organizing this event and coordinating this special issue. We also would like to thank Prof. Jun Fang, who led our team to win the 1st prize of the GBSense competition and helped revise this paper.
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Jilin Wang received the BS degree in communication engineering from the Tiangong University, China in 2019. He is currently working toward the PhD degree with the National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), China. His current research interests include compressed sensing and signal processing.
Yinsen Huang received the BS degree from the Xidian University, China in 2019. He is currently working toward the MS degree with the University of Electronic Science and Technology of China, China. His research interests include massive multiple-input and multiple-output (MIMO) communications, and deep learning.
Bin Wang received his ME degree in optical engineering from the Xi’an University of Technology, China in 2015, and the PhD degree in communication and information system from University of Electronic Science and Technology of China (UESTC), China in 2021. He is currently a post-doc at UESTC. His research interests include signal processing and optimization theory.
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Wang, J., Huang, Y. & Wang, B. Sub-Nyquist sampling-based wideband spectrum sensing: a compressed power spectrum estimation approach. Front. Comput. Sci. 18, 182501 (2024). https://doi.org/10.1007/s11704-022-2158-6
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DOI: https://doi.org/10.1007/s11704-022-2158-6