東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Sensing and Learning in Cognitive Ra...

Zhou, Lei.

FindBook

Google Book

Amazon

博客來

Sensing and Learning in Cognitive Radio Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sensing and Learning in Cognitive Radio Systems./
作者:	Zhou, Lei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	124 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248847
ISBN:	9780355093223

Sensing and Learning in Cognitive Radio Systems.
Zhou, Lei.

Sensing and Learning in Cognitive Radio Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 124 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2017.

Cognitive radio (CR) describes an intelligent wireless communication system that can understand radio system itself as well as its environment by learning, and can produce the corresponding responses adaptively based on interactions with the environment. In this dissertation, two specific CR problems are investigated and new methods are presented to enhance the learning and sensing ability.

ISBN: 9780355093223Subjects--Topical Terms:

649834
Electrical engineering.

Sensing and Learning in Cognitive Radio Systems.
LDR:02859nmm a2200349 4500 001 2162235
005 20180928111500.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355093223
035 $a (MiAaPQ)AAI10248847
035 $a (MiAaPQ)stevens:10372
035 $a AAI10248847
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhou, Lei. $3 1272475
245 1 0 $a Sensing and Learning in Cognitive Radio Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 124 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Hong Man.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2017.
520 $a Cognitive radio (CR) describes an intelligent wireless communication system that can understand radio system itself as well as its environment by learning, and can produce the corresponding responses adaptively based on interactions with the environment. In this dissertation, two specific CR problems are investigated and new methods are presented to enhance the learning and sensing ability.
520 $a The first problem is learning-based automatic modulation classification (AMC), which uses machine learning technique as the learning engine of cognitive radio to estimate the modulation scheme from a sequence of noisy observations automatically, blindly and rapidly. Two specific neural network method MLP and SOM are introduced as the classier, and various versions are proposed to produce better performance.
520 $a The second problem is the characterization of the sensing behavior of cognitive radios in wide-band spectrum sensing. Different from traditional spectrum sensing methods, an emerging technique called Compressive Sensing (CS) is introduced to cognitive radio domain so that only compressive measurements are needed in real implementation for implementation and bandwidth efficiency. The Orthogonal Matching Pursuit (OMP) is introduced as the reconstruction algorithm in CS and a series of novel algorithms based on OMP are also proposed to better accommodate the practical continuous signals and the signals with channel fading when in cooperative sensing.
520 $a Furthermore, instead of studying cognitive radio's learning and sensing behavior separately, this dissertation investigates approaches in AMC that can combine the advantage of both sensing and learning capabilities. A method that incorporates sensing technique in CR learning engine is presented based on AMC applications. This method is further extended to a cooperative scenario when multiple users exist.
590 $a School code: 0733.
650 4 $a Electrical engineering. $3 649834
650 4 $a Engineering. $3 586835
650 4 $a Artificial intelligence. $3 516317
690 $a 0544
690 $a 0537
690 $a 0800
710 2 $a Stevens Institute of Technology. $b Electrical Engineer. $3 3180817
773 0 $t Dissertation Abstracts International $g 78-12B(E).
790 $a 0733
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248847