Abstract
The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from −19dB to −12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs.
Highlights
The emergence of cognitive wireless sensor networks (CWSN) has significantly extended the applications of wireless sensor networks (WSNs) due to their ability to use a dynamic spectrum to increase the available spectrum resources
We have shown that the spectrum sensing algorithm which is based on multi-resolution singular
We have shown that the spectrum sensing algorithm which is based on multi-resolution spectral entropy can greatly improve detection performance, especially at low signal-to-noise ratio (SNR), and the algorithm singular spectral entropy can greatly improve detection performance, especially at low SNR, and is both sensitive and reliable when we study spectrum sensing based on a non-cooperative design
Summary
The emergence of cognitive wireless sensor networks (CWSN) has significantly extended the applications of wireless sensor networks (WSNs) due to their ability to use a dynamic spectrum to increase the available spectrum resources. Spectrum sensing technology is the basis of spectrum sharing, which can acquire spectrum use information in wireless networks by using signal detection and processing methods. In other words, it can dynamically detect the change of the communication environment and obtain an available communication channel in time. We have previously studied various types of cooperative spectrum sensing architectures and algorithms; the requirement of node computing power has been reduced, the problem of the high requirement of communication ability has still not been solved. Kinds of non-cooperative spectrum sensing algorithms have been studied, but the detection performance cannot meet the requirements of wireless sensor networks
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