Kernel-based learning for statistical signal processing in cognitive radio networks: Theoretical foundations, example applications, and future directions

Guoru Ding, Qihui Wu, Yu Dong Yao, Jinlong Wang, Yingying Chen

Research output: Contribution to journalArticlepeer-review

178 Scopus citations

Abstract

Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.

Original languageEnglish
Article number6530744
Pages (from-to)126-136
Number of pages11
JournalIEEE Signal Processing Magazine
Volume30
Issue number4
DOIs
StatePublished - 2013

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