COGNITIVE RADIO SPECTRUM SENSING USING ONLINE DICTIONARY LEARNING AND DEEP LAYERED ARCHITECTURES
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Author/Creator ORCID
Date
2017-01-01
Type of Work
Department
Computer Science and Electrical Engineering
Program
Engineering, Electrical
Citation of Original Publication
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Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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Abstract
Dictionary learning based on sparse coding has exhibited excellent performance for various tasks such as denoising, prediction and classication with diverse applictions. However, sparse coding-based dictionary learning does not capture potential clusters of subspaces in the data. In this work, dictionary learning based on both sparsity and low rank properties is formulated and ecient solution methods are derived in both batch and online implementations. The algorithms are applied to a spectrum sensing problem for cognitive radios. The numerical experiments illustrate the merit of the novel approach. Furthermore, the algorithm is extended to the spectrum prediction problem, where the future interference levels are forecasted. Finally, a RF signal classciation problem is tackled using a deep layered architecture combining the scattering transform and the convolutional neural network.