LEADER 03785nam 22005175 450 001 9910337647503321 005 20200704025359.0 010 $a3-030-00290-X 024 7 $a10.1007/978-3-030-00290-9 035 $a(CKB)5120000000121536 035 $a(MiAaPQ)EBC5560025 035 $a(DE-He213)978-3-030-00290-9 035 $a(PPN)23146245X 035 $a(EXLCZ)995120000000121536 100 $a20181019d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData-Driven Wireless Networks $eA Compressive Spectrum Approach /$fby Yue Gao, Zhijin Qin 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (104 pages) 225 1 $aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 311 $a3-030-00289-6 330 $aThis SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security. Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing. This SpringerBrief provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks. Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief very useful as a short reference or study guide book. Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well. 410 0$aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 606 $aWireless communication systems 606 $aMobile communication systems 606 $aElectrical engineering 606 $aWireless and Mobile Communication$3https://scigraph.springernature.com/ontologies/product-market-codes/T24100 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aWireless communication systems. 615 0$aMobile communication systems. 615 0$aElectrical engineering. 615 14$aWireless and Mobile Communication. 615 24$aCommunications Engineering, Networks. 676 $a621.384560285625 676 $a006.25 700 $aGao$b Yue$4aut$4http://id.loc.gov/vocabulary/relators/aut$0994999 702 $aQin$b Zhijin$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337647503321 996 $aData-Driven Wireless Networks$92296060 997 $aUNINA