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 MoreMetadata
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