語系:
繁體中文
English
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
A Machine Learning Method for Positi...
~
Liu, Chong .
FindBook
Google Book
Amazon
博客來
A Machine Learning Method for Positioning in the 5G Cellular Networks.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
A Machine Learning Method for Positioning in the 5G Cellular Networks./
作者:
Liu, Chong .
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
93 p.
附註:
Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:
Dissertations Abstracts International81-10B.
標題:
Electrical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834564
ISBN:
9798607320959
A Machine Learning Method for Positioning in the 5G Cellular Networks.
Liu, Chong .
A Machine Learning Method for Positioning in the 5G Cellular Networks.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 93 p.
Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Thesis (Ph.D.)--The George Washington University, 2020.
This item must not be sold to any third party vendors.
Technologies in the developing of 5G-communication system provide interesting prospects, which are beneficial for positioning. The substantial increase in the number of mobile users (MUs) and their demand on higher data rates and volumes, create challenges on the efficient distribution of limited resources in future 5G networks. Indoor and outdoor positioning of users could be of benefit in spectral balancing, power efficiency and geographic routing by base stations (BS). Although existing positioning techniques can mostly overcome problems caused by path loss, background noise and Doppler effects, multiple paths in complex indoor or outdoor environments present additional challenges. In addition, dynamic environments in the outdoor will also bring some troubles in the localization of cellular network.In the first part of this dissertation, we are trying to introduce some new technologies employed in the 5G systems, which will be added in our localization system design. The emerging technology of flexible massive multiple-input multiple-output (MIMO) through multiple antennas reduce the interference while transmitting more information from many more antennas at the same time. We will employ the advantages of MIMO to transmit and receive more signals of mobile users and thereby increase the efficiency of localization. Also several beamforming designs have been proposed in existing literature for channel communications that could be collaborated with the conventional MIMO systems. Different beamforming techniques can be flexibly employed in adapting the distribution of wireless bandwidth.In the second part of the dissertation, in order to address the detailed problems, we propose a BeamMaP positioning system to locate users and steer the beams efficiently in a distributed massive MIMO system. To simulate a realistic environment, we evaluate the positioning accuracy with channel fingerprints from uplink received signal strength (RSS) data, including line-of-sight (LoS) and Non-line-of-sight (NLoS), in the training data sets. Analytical models prove the better performance compared with conventional positioning system.In an effort to improve the flexibility in the outdoor environment, we propose design an improved adaptive BeamMaP that can instantaneously locate users in dynamic environment urban in the third part of dissertation. In addition, based on the adaptive beamforming, we employ the Rice distribution to sample the current mobile users locations in the testing data sets. Our simulation results achieve reduced root-mean-squared estimation error (RMSE) performance with increasing volume of training data. The results demonstrate the effectiveness of the adaptive beamforming model in the test process.Finally, we discuss the inadequate consideration of our proposed methods and expand our motivation to improve the better performance for our future works in the last chapter.
ISBN: 9798607320959Subjects--Topical Terms:
649834
Electrical engineering.
Subjects--Index Terms:
5G networks
A Machine Learning Method for Positioning in the 5G Cellular Networks.
LDR
:04096nmm a2200373 4500
001
2266171
005
20200608114814.5
008
220629s2020 ||||||||||||||||| ||eng d
020
$a
9798607320959
035
$a
(MiAaPQ)AAI27834564
035
$a
AAI27834564
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Liu, Chong .
$3
3543355
245
1 0
$a
A Machine Learning Method for Positioning in the 5G Cellular Networks.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2020
300
$a
93 p.
500
$a
Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500
$a
Advisor: Helgert, Hermann J.
502
$a
Thesis (Ph.D.)--The George Washington University, 2020.
506
$a
This item must not be sold to any third party vendors.
520
$a
Technologies in the developing of 5G-communication system provide interesting prospects, which are beneficial for positioning. The substantial increase in the number of mobile users (MUs) and their demand on higher data rates and volumes, create challenges on the efficient distribution of limited resources in future 5G networks. Indoor and outdoor positioning of users could be of benefit in spectral balancing, power efficiency and geographic routing by base stations (BS). Although existing positioning techniques can mostly overcome problems caused by path loss, background noise and Doppler effects, multiple paths in complex indoor or outdoor environments present additional challenges. In addition, dynamic environments in the outdoor will also bring some troubles in the localization of cellular network.In the first part of this dissertation, we are trying to introduce some new technologies employed in the 5G systems, which will be added in our localization system design. The emerging technology of flexible massive multiple-input multiple-output (MIMO) through multiple antennas reduce the interference while transmitting more information from many more antennas at the same time. We will employ the advantages of MIMO to transmit and receive more signals of mobile users and thereby increase the efficiency of localization. Also several beamforming designs have been proposed in existing literature for channel communications that could be collaborated with the conventional MIMO systems. Different beamforming techniques can be flexibly employed in adapting the distribution of wireless bandwidth.In the second part of the dissertation, in order to address the detailed problems, we propose a BeamMaP positioning system to locate users and steer the beams efficiently in a distributed massive MIMO system. To simulate a realistic environment, we evaluate the positioning accuracy with channel fingerprints from uplink received signal strength (RSS) data, including line-of-sight (LoS) and Non-line-of-sight (NLoS), in the training data sets. Analytical models prove the better performance compared with conventional positioning system.In an effort to improve the flexibility in the outdoor environment, we propose design an improved adaptive BeamMaP that can instantaneously locate users in dynamic environment urban in the third part of dissertation. In addition, based on the adaptive beamforming, we employ the Rice distribution to sample the current mobile users locations in the testing data sets. Our simulation results achieve reduced root-mean-squared estimation error (RMSE) performance with increasing volume of training data. The results demonstrate the effectiveness of the adaptive beamforming model in the test process.Finally, we discuss the inadequate consideration of our proposed methods and expand our motivation to improve the better performance for our future works in the last chapter.
590
$a
School code: 0075.
650
4
$a
Electrical engineering.
$3
649834
650
4
$a
Computer science.
$3
523869
650
4
$a
Applied mathematics.
$3
2122814
653
$a
5G networks
653
$a
Adaptive beamforming
653
$a
Machine learning
653
$a
Outdoor localization
653
$a
Wireless networks
690
$a
0544
690
$a
0984
690
$a
0364
710
2
$a
The George Washington University.
$b
Electrical Engineering.
$3
1035543
773
0
$t
Dissertations Abstracts International
$g
81-10B.
790
$a
0075
791
$a
Ph.D.
792
$a
2020
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834564
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9418405
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入