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• W2891200114 abstract "The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity." @default.
• W2891200114 created "2018-09-27" @default.
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• W2891200114 date "2018-09-08" @default.
• W2891200114 modified "2023-10-10" @default.
• W2891200114 title "A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing" @default.
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• W2891200114 doi "https://doi.org/10.3390/s18093011" @default.
• W2891200114 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6164262" @default.
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