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Machine Learning Based Spectrum Deci...

Araseethota Manjunatha, Koushik.

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Machine Learning Based Spectrum Decision in Cognitive Radio Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Based Spectrum Decision in Cognitive Radio Networks./
Author:	Araseethota Manjunatha, Koushik.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	115 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745767
ISBN:	9780438040014

Machine Learning Based Spectrum Decision in Cognitive Radio Networks.
Araseethota Manjunatha, Koushik.

Machine Learning Based Spectrum Decision in Cognitive Radio Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 115 p.

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--The University of Alabama, 2018.

The cognitive radio network (CRN) is considered as one of the promising solutions to address the issue of spectrum scarcity and effective spectrum utilization. In a CRN the Secondary User (SU) is allowed to occupy the spectrum which is temporarily not used by the Primary User (PU). Frequent interruptions from the PUs is the fundamental issue in CRN. The interruption forces SU to perform handoff to another idle channel. On the other hand, spectrum handoff can occur due to the mobility of the node. Hence, CRNs needs a smart spectrum decision scheme to timely switch the channels. An important issue in spectrum decision is spectrum handoff. Since the SU's spectrum usage is constrained by the PU's traffic pattern, it should carefully choose the right handoff time. To increase the overall performance of the SU in the long term we use several machine learning algorithms in spectrum decision and compare it with the myopic decision which tries to achieve maximum performance in the short run.

ISBN: 9780438040014Subjects--Topical Terms:

621879
Computer engineering.

Machine Learning Based Spectrum Decision in Cognitive Radio Networks.
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245 1 0 $a Machine Learning Based Spectrum Decision in Cognitive Radio Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Advisers: Fei Hu; Sunil Kumar.
502 $a Thesis (Ph.D.)--The University of Alabama, 2018.
520 $a The cognitive radio network (CRN) is considered as one of the promising solutions to address the issue of spectrum scarcity and effective spectrum utilization. In a CRN the Secondary User (SU) is allowed to occupy the spectrum which is temporarily not used by the Primary User (PU). Frequent interruptions from the PUs is the fundamental issue in CRN. The interruption forces SU to perform handoff to another idle channel. On the other hand, spectrum handoff can occur due to the mobility of the node. Hence, CRNs needs a smart spectrum decision scheme to timely switch the channels. An important issue in spectrum decision is spectrum handoff. Since the SU's spectrum usage is constrained by the PU's traffic pattern, it should carefully choose the right handoff time. To increase the overall performance of the SU in the long term we use several machine learning algorithms in spectrum decision and compare it with the myopic decision which tries to achieve maximum performance in the short run.
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650 4 $a Electrical engineering. $3 649834
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