國立虎尾科技大學 |

Sensing and Learning in Cognitive Radio Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Sensing and Learning in Cognitive Radio Systems./
作者:	Zhou, Lei.
面頁冊數:	1 online resource (124 pages)
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:	click for full text (PQDT)
ISBN:	9780355093223

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

Sensing and Learning in Cognitive Radio Systems. - 1 online resource (124 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355093223Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Sensing and Learning in Cognitive Radio Systems.
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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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Engineering. $3 561152
650 4 $a Artificial intelligence. $3 559380
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710 2 $a Stevens Institute of Technology. $b Electrical Engineer. $3 1190572
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