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

作者：Hu, Zhuhua; Bai, Yong^*; Huang, Mengxing; Xie, Mingshan; Zhao, Yaochi

来源：Sensors (Switzerland), 2018, 18(9): 3011.

DOI：10.3390/s18093011

• cognitive radio network
• singular value decomposition
• cooperative wideband spectrum sensing
• transmission loss
• modulated wideband converter
• progressive support selection

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.

• 出版日期2018-9
• 单位海南大学