國立虎尾科技大學 |

Data-Driven Wireless Networks = A Compressive Spectrum Approach /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data-Driven Wireless Networks/ by Yue Gao, Zhijin Qin.
Reminder of title:	A Compressive Spectrum Approach /
Author:	Gao, Yue.
other author:	Qin, Zhijin.
Description:	XIX, 93 p. 35 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Wireless communication systems. -
Online resource:	https://doi.org/10.1007/978-3-030-00290-9
ISBN:	9783030002909

Data-Driven Wireless Networks = A Compressive Spectrum Approach /
Gao, Yue.

Data-Driven Wireless NetworksA Compressive Spectrum Approach /[electronic resource] :by Yue Gao, Zhijin Qin. - 1st ed. 2019. - XIX, 93 p. 35 illus. in color.online resource. - SpringerBriefs in Electrical and Computer Engineering,2191-8112. - SpringerBriefs in Electrical and Computer Engineering,.

This 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.

ISBN: 9783030002909

Standard No.: 10.1007/978-3-030-00290-9doiSubjects--Topical Terms:

562740
Wireless communication systems.

LC Class. No.: TK5103.2-.4885

Dewey Class. No.: 384.5

Data-Driven Wireless Networks = A Compressive Spectrum Approach /
LDR:03156nam a22003855i 4500 001 1007152
003 DE-He213
005 20200704025359.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030002909 $9 978-3-030-00290-9
024 7 $a 10.1007/978-3-030-00290-9 $2 doi
035 $a 978-3-030-00290-9
050 4 $a TK5103.2-.4885
072 7 $a TJKW $2 bicssc
072 7 $a TEC061000 $2 bisacsh
072 7 $a TJKW $2 thema
082 0 4 $a 384.5 $2 23
100 1 $a Gao, Yue. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1300866
245 1 0 $a Data-Driven Wireless Networks $h [electronic resource] : $b A Compressive Spectrum Approach / $c by Yue Gao, Zhijin Qin.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XIX, 93 p. 35 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Electrical and Computer Engineering, $x 2191-8112
520 $a This 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.
650 0 $a Wireless communication systems. $3 562740
650 0 $a Mobile communication systems. $3 562917
650 0 $a Electrical engineering. $3 596380
650 1 4 $a Wireless and Mobile Communication. $3 1207058
650 2 4 $a Communications Engineering, Networks. $3 669809
700 1 $a Qin, Zhijin. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1300867
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030002893
776 0 8 $i Printed edition: $z 9783030002916
830 0 $a SpringerBriefs in Electrical and Computer Engineering, $x 2191-8112 $3 1253713
856 4 0 $u https://doi.org/10.1007/978-3-030-00290-9
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)