AI Article Synopsis
- The cognitive wireless sensor network (CWSN) relies on effective spectrum sensing technology to enable spectrum sharing, yet current methods struggle to meet its demands.
- A new non-cooperative spectrum sensing algorithm is introduced, utilizing multi-resolution techniques, phase space reconstruction, and singular spectrum entropy to improve detection of narrowband wireless signals.
- Simulation results demonstrate significant enhancements in detection probability, especially at low signal-to-noise ratios (SNR), indicating potential advancements for CWSN and wireless sensor networks overall.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538997 | PMC |
| http://dx.doi.org/10.3390/s19092174 | DOI Listing |
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