Broadband Cooperative Spectrum Sensing Based on Distributed Modulated Wideband Converter

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 10; p. 1602
• Main Authors: Xu, Ziyong, Li, Zhi, Li, Jian
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.09.2016; MDPI
• Subjects: Broadband; Channels; cognitive radio; compressed sensing; Converters; cooperative spectrum sensing; Detection; distributed modulated wideband converter; frequency support; Geography; Phase shift; Recovery; Sampling; Sensors; Sparsity; transmission loss; Wideband; transmission loss; cognitive radio; compressed sensing; distributed modulated wideband converter; frequency support; cooperative spectrum sensing
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16101602

The modulated wideband converter (MWC) is a kind of sub-Nyquist sampling system which is developed from compressed sensing theory. It accomplishes highly accurate broadband sparse signal recovery by multichannel sub-Nyquist sampling sequences. However, when the number of sparse sub-bands becomes large, the amount of sampling channels increases proportionally. Besides, it is very hard to adjust the number of sampling channels when the sparsity changes, because its undersampling board is designed by a given sparsity. Such hardware cost and inconvenience are unacceptable in practical applications. This paper proposes a distributed modulated wideband converter (DMWC) scheme innovatively, which regards one sensor node as one sampling channel and combines MWC technology with a broadband cooperative spectrum sensing network perfectly. Being different from the MWC scheme, DMWC takes phase shift and transmission loss into account in the input terminal, which are unavoidable in practical application. Our scheme is not only able to recover the support of broadband sparse signals quickly and accurately, but also reduces the hardware cost of the single node drastically. Theoretical analysis and numerical simulations show that phase shift has no influence on the recovery of frequency support, but transmission loss degrades the recovery performance to a different extent. Nevertheless, we can increase the amount of cooperative nodes and select satisfactory nodes by a different transmission distance to improve the recovery performance. Furthermore, we can adjust the amount of cooperative nodes flexibly when the sparsity changes. It indicates DMWC is extremely effective in the broadband cooperative spectrum sensing network.