A New Classification-Like Scheme for Spectrum Sensing Using Spectral Correlation and Stacked Denoising Autoencoders

Hang LIU  Xu ZHU  Takeo FUJII  

IEICE TRANSACTIONS on Communications   Vol.E101-B   No.11   pp.2348-2361
Publication Date: 2018/11/01
Publicized: 2018/04/25
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBP3447
Type of Manuscript: PAPER
Category: Terrestrial Wireless Communication/Broadcasting Technologies
spectrum sensing,  cyclostationary analysis,  stacked denoising autoencoders,  

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In this paper, we propose a novel primary user detection scheme for spectrum sensing in cognitive radio. Inspired by the conventional signal classification approach, the spectrum sensing is translated into a classification problem. On the basis of feature-based classification, the spectral correlation of a second-order cyclostationary analysis is applied as the feature extraction method, whereas a stacked denoising autoencoders network is applied as the classifier. Two training methods for signal detection, interception-based detection and simulation-based detection, are considered, for different prior information and implementation conditions. In an interception-based detection method, inspired by the two-step sensing, we obtain training data from the interception of actual signals after a sophisticated sensing procedure, to achieve detection without priori information. In addition, benefiting from practical training data, this interception-based detection is superior under actual transmission environment conditions. The alternative, a simulation-based detection method utilizes some undisguised parameters of the primary user in the spectrum of interest. Owing to the diversified predetermined training data, simulation-based detection exhibits transcendental robustness against harsh noise environments, although it demands a more complicated classifier network structure. Additionally, for the above-described training methods, we discuss the classifier complexity over implementation conditions and the trade-off between robustness and detection performance. The simulation results show the advantages of the proposed method over conventional spectrum-sensing schemes.