A Novel Low-Complexity Cyclostationary Feature Detection Using Sub-Nyquist Samples for Wideband Spectrum Sensing

摘要

In this paper, we propose a novel low-complexity scheme to extract cyclic features of wideband signals from sub-Nyquist samples as Nyquist rates push contemporary analog-to-digital converters to their performance limits. Due to the sparse spectrum occupancy in wideband, sub-Nyquist sampling is performed and cyclostationary feature extraction is achieved at baseband to identify the modulation scheme with low computational complexity. The multichannel sub-Nyquist sampling structure of the modulated wideband converter (MWC) is used to perform spectrum sensing. Automatic modulation identification and classification are done based on cyclostationary feature detection for the efficient use of available spectrum bands. The computational complexity is reduced as the aliased copy of the signal in baseband obtained in each channel of MWC is only used to estimate the cyclic spectrum of the signal and identify the modulation scheme. Simulations performed validate the proposed method for various modulation schemes.