Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation Algorithm | IEEE Conference Publication | IEEE Xplore

Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation Algorithm


Abstract:

Cyclic spectrum is a basic tool to study the cyclostationary of signal. It has the advantages of high resolution, anti-interference ability and low channel environment se...Show More

Abstract:

Cyclic spectrum is a basic tool to study the cyclostationary of signal. It has the advantages of high resolution, anti-interference ability and low channel environment sensitivity. These properties are beneficial to process digital modulated signals. There are two time-smoothed algorithms: the FFT Accumulation Method (FAM) and the Strip Spectral Correlation Algorithm (SSCA). SSCA is suitable for fast estimation and engineering realizable, while FAM has shortcoming of complex computation. But both methods need high sampling rate that requires high performance analog-to-digital converter. To improve the efficiency of cyclic spectrum estimation, we propose CS-SSCA algorithm in the framework of Compressive Sensing (CS). The cyclic spectrum can be recovered via compressed samples by SSCA. The theoretical analysis and simulation results prove that the improved algorithm displays better performance than CS-FAM under the same conditions.
Date of Conference: 25-29 June 2018
Date Added to IEEE Xplore: 30 August 2018
ISBN Information:
Electronic ISSN: 2376-6506
Conference Location: Limassol, Cyprus

References

References is not available for this document.