A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing.

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing.
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DOI:
10.3390/s18093011
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发表时间:
2018-09-08
期刊:
Sensors (Basel, Switzerland)
影响因子:
--
通讯作者:
Zhao Y
Zhao Y
中科院分区:
其他
文献类型:
--
作者:
Hu Z;Bai Y;Huang M;Xie M;Zhao Y

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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.
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