國立虎尾科技大學 |

Optimal Resource Scheduling for Energy-Efficient Next Generation Wireless Networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimal Resource Scheduling for Energy-Efficient Next Generation Wireless Networks./
Author:	Kim, Taewoon.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	173 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Contained By:	Dissertation Abstracts International80-02B(E).
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10841690
ISBN:	9780438415317

Optimal Resource Scheduling for Energy-Efficient Next Generation Wireless Networks.
Kim, Taewoon.

Optimal Resource Scheduling for Energy-Efficient Next Generation Wireless Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 173 p.

Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.

Thesis (Ph.D.)--Iowa State University, 2018.

Cellular networks can provide highly available and reliable communication links to the Internet of Things (IoT) applications, letting the connected Things paradigm gain much more momentum than ever. Also, the rich information collected from the Things with sensing capabilities can guide the network operator to an unforeseen direction, allowing the underlying cellular networks to be further optimized. In this regard, the cellular networks and IoT are conceived as the key components of the beyond-4G and future 5G networks. Therefore, in this dissertation, we study each of the two components in depth, focusing on how to optimize the networking resources for the quality service and better energy-efficiency. To begin with, we study the heterogeneous cellular network architecture which is a major enhancement to the current 4G network by means of the base station (BS) densification and traffic offloading. In particular, the densely deployed short-range, low-power smallcell base stations (SBSs) can significantly improve the frequency reuse, throughput performance and the energy-efficiency. We then study the heterogeneous C-RAN (cloud radio access network), which is one of the core enablers of the next generation 5G cellular networks. In particular, with the high availability provided by the long-range macro BS (MBS), the heterogeneous C-RAN (H-CRAN) can effectively enhance the overall resource utilization compared to the conventional C-RANs. In each study, we propose an optimal resource scheduling and service provisioning scheme to provide a quality service to users in a resource-efficient manner. In addition, we carry out two studies for the Internet of Things (IoT) networks operating with the IEEE 802.11ah standard. Specifically, we introduce energy-efficient device management algorithms for the battery-operated, resource-constrained IoT sensor devices to prolong their lifetime by optimally scheduling their activation. The enhanced power saving mechanism and the optimal sensing algorithm that we propose in each study can effectively improve both the energy-efficiency of the IoT devices and the lifetime of the entire network.

ISBN: 9780438415317Subjects--Topical Terms:

569006
Computer engineering.

Optimal Resource Scheduling for Energy-Efficient Next Generation Wireless Networks.
LDR:03141nam a2200313 4500 001 931685
005 20190716101635.5
008 190815s2018 ||||||||||||||||| ||eng d
020 $a 9780438415317
035 $a (MiAaPQ)AAI10841690
035 $a (MiAaPQ)iastate:17536
035 $a AAI10841690
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kim, Taewoon. $0 (orcid)0000-0002-7811-5022 $3 1213892
245 1 0 $a Optimal Resource Scheduling for Energy-Efficient Next Generation Wireless Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 173 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 $a Advisers: Daji Qiao; Jien M. Chang.
502 $a Thesis (Ph.D.)--Iowa State University, 2018.
520 $a Cellular networks can provide highly available and reliable communication links to the Internet of Things (IoT) applications, letting the connected Things paradigm gain much more momentum than ever. Also, the rich information collected from the Things with sensing capabilities can guide the network operator to an unforeseen direction, allowing the underlying cellular networks to be further optimized. In this regard, the cellular networks and IoT are conceived as the key components of the beyond-4G and future 5G networks. Therefore, in this dissertation, we study each of the two components in depth, focusing on how to optimize the networking resources for the quality service and better energy-efficiency. To begin with, we study the heterogeneous cellular network architecture which is a major enhancement to the current 4G network by means of the base station (BS) densification and traffic offloading. In particular, the densely deployed short-range, low-power smallcell base stations (SBSs) can significantly improve the frequency reuse, throughput performance and the energy-efficiency. We then study the heterogeneous C-RAN (cloud radio access network), which is one of the core enablers of the next generation 5G cellular networks. In particular, with the high availability provided by the long-range macro BS (MBS), the heterogeneous C-RAN (H-CRAN) can effectively enhance the overall resource utilization compared to the conventional C-RANs. In each study, we propose an optimal resource scheduling and service provisioning scheme to provide a quality service to users in a resource-efficient manner. In addition, we carry out two studies for the Internet of Things (IoT) networks operating with the IEEE 802.11ah standard. Specifically, we introduce energy-efficient device management algorithms for the battery-operated, resource-constrained IoT sensor devices to prolong their lifetime by optimally scheduling their activation. The enhanced power saving mechanism and the optimal sensing algorithm that we propose in each study can effectively improve both the energy-efficiency of the IoT devices and the lifetime of the entire network.
590 $a School code: 0097.
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
650 4 $a Electrical engineering. $3 596380
690 $a 0464
690 $a 0984
690 $a 0544
710 2 $a Iowa State University. $b Electrical and Computer Engineering. $3 1179387
773 0 $t Dissertation Abstracts International $g 80-02B(E).
790 $a 0097
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10841690