國立虎尾科技大學 |

Transceiver Designs for Enhanced Vehicular and Non-terrestrial Communications : = Developments and Deployments.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Transceiver Designs for Enhanced Vehicular and Non-terrestrial Communications :/
其他題名:	Developments and Deployments.
作者:	Lin, Chia-Hung.
面頁冊數:	1 online resource (148 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Satellite communications. -
電子資源:	click for full text (PQDT)
ISBN:	9798379871666

Transceiver Designs for Enhanced Vehicular and Non-terrestrial Communications : = Developments and Deployments.
Lin, Chia-Hung.

Transceiver Designs for Enhanced Vehicular and Non-terrestrial Communications :Developments and Deployments. - 1 online resource (148 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2023.

Includes bibliographical references

In the era of next-generation communications, or 6G, the ability to provide seamless connectivity to users anywhere and anytime is crucial. While 5G systems have already provided stable and fast communication capabilities for conventional mobile communications scenarios, the challenge now lies in extending these guarantees to fast-moving objects in vehicular communications and non-terrestrial communications. These scenarios differ from mobile communications as the fast-changing wireless channels caused by fast-moving objects significantly reduce the response time of transceivers. Therefore, suitable algorithms are needed that can offer high performance, low complexity, and fast reconfigurable attributes to perform transceiver operations based on the underlying fast-changing wireless channels.Over the past decade, the development of deep learning (DL) algorithms has transformed our daily lives and enabled new applications. These technologies have also driven the evolution of next-generation communication systems from a communication engineering perspective. DL algorithms can be used as powerful tools to solve problems and create DL-aided communication system designs with low complexity, high performance, and fast reconfigurable attributes. Additionally, as users perform training or inference on DL algorithms, next-generation communication systems are expected to better support those learning processes by utilizing in-network communication and computing resources. After a comprehensive literature survey, we noticed that DL-based communication system designs are particularly well-suited for aiding transceiver designs in vehicular and non-terrestrial communications, especially when conventional optimization-based algorithms encounter imprecise system models or computationally demanding issues. As such, this thesis focuses on developing learning-based transceiver designs to better serve vehicular and non-terrestrial communications, along with framework designs to facilitate the real deployment of these proposed designs. The first part of this thesis presents dedicated designs for vehicular communications, with a focus on vehicle-to-vehicle (V2V) communications in Chapters 2, 3, and 4. The thesis then proposes a platform for system designers to deploy the proposed designs with 6G infrastructure in Chapter 5, aiming to provide next-generation communication systems that better aid the training and inference processes of users. To handle the communication and computation costs in the learning process, the proposed framework can dynamically orchestrate resources among heterogeneous physical units, including in-network mobile devices, edge and cloud computing centers, to efficiently fulfill deep learning objectives. The framework can also dynamically allocate resources and intelligently assign communications and computation tasks to these units. The second part of this thesis extends these designs to satellite communications, taking into account the special channel features in this context. Using the extra information provided by the geometry relationship of satellite communication scenarios, this thesis discusses the satellite transceiver designs incorporating the geometry relationship to achieve better different communication quality of service indicators in Chapters 6 and 7. Through the proposed designs, this thesis aims to provide next-generation communication systems that offer greater efficiency to users, enabled by the power of advanced DL-based algorithms. Additionally, the thesis aims to present next-generation communication system designs that facilitate training and inference processes when users deploy learning algorithms in distributed environments.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379871666Subjects--Topical Terms:

1413505
Satellite communications.
Index Terms--Genre/Form:

554714
Electronic books.

Transceiver Designs for Enhanced Vehicular and Non-terrestrial Communications : = Developments and Deployments.
LDR:04998ntm a22003377 4500 001 1145112
005 20240617111347.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379871666
035 $a (MiAaPQ)AAI30516428
035 $a (MiAaPQ)NCState_Univ18402040811
035 $a AAI30516428
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Lin, Chia-Hung. $3 1470329
245 1 0 $a Transceiver Designs for Enhanced Vehicular and Non-terrestrial Communications : $b Developments and Deployments.
264 0 $c 2023
300 $a 1 online resource (148 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Lin, Shih-Chun.
502 $a Thesis (Ph.D.)--North Carolina State University, 2023.
504 $a Includes bibliographical references
520 $a In the era of next-generation communications, or 6G, the ability to provide seamless connectivity to users anywhere and anytime is crucial. While 5G systems have already provided stable and fast communication capabilities for conventional mobile communications scenarios, the challenge now lies in extending these guarantees to fast-moving objects in vehicular communications and non-terrestrial communications. These scenarios differ from mobile communications as the fast-changing wireless channels caused by fast-moving objects significantly reduce the response time of transceivers. Therefore, suitable algorithms are needed that can offer high performance, low complexity, and fast reconfigurable attributes to perform transceiver operations based on the underlying fast-changing wireless channels.Over the past decade, the development of deep learning (DL) algorithms has transformed our daily lives and enabled new applications. These technologies have also driven the evolution of next-generation communication systems from a communication engineering perspective. DL algorithms can be used as powerful tools to solve problems and create DL-aided communication system designs with low complexity, high performance, and fast reconfigurable attributes. Additionally, as users perform training or inference on DL algorithms, next-generation communication systems are expected to better support those learning processes by utilizing in-network communication and computing resources. After a comprehensive literature survey, we noticed that DL-based communication system designs are particularly well-suited for aiding transceiver designs in vehicular and non-terrestrial communications, especially when conventional optimization-based algorithms encounter imprecise system models or computationally demanding issues. As such, this thesis focuses on developing learning-based transceiver designs to better serve vehicular and non-terrestrial communications, along with framework designs to facilitate the real deployment of these proposed designs. The first part of this thesis presents dedicated designs for vehicular communications, with a focus on vehicle-to-vehicle (V2V) communications in Chapters 2, 3, and 4. The thesis then proposes a platform for system designers to deploy the proposed designs with 6G infrastructure in Chapter 5, aiming to provide next-generation communication systems that better aid the training and inference processes of users. To handle the communication and computation costs in the learning process, the proposed framework can dynamically orchestrate resources among heterogeneous physical units, including in-network mobile devices, edge and cloud computing centers, to efficiently fulfill deep learning objectives. The framework can also dynamically allocate resources and intelligently assign communications and computation tasks to these units. The second part of this thesis extends these designs to satellite communications, taking into account the special channel features in this context. Using the extra information provided by the geometry relationship of satellite communication scenarios, this thesis discusses the satellite transceiver designs incorporating the geometry relationship to achieve better different communication quality of service indicators in Chapters 6 and 7. Through the proposed designs, this thesis aims to provide next-generation communication systems that offer greater efficiency to users, enabled by the power of advanced DL-based algorithms. Additionally, the thesis aims to present next-generation communication system designs that facilitate training and inference processes when users deploy learning algorithms in distributed environments.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Satellite communications. $3 1413505
650 4 $a Privacy. $3 575491
650 4 $a Performance evaluation. $3 1246613
650 4 $a Low earth orbit satellites. $3 1470330
650 4 $a Systems development. $3 1470331
650 4 $a Aerospace engineering. $3 686400
650 4 $a Systems science. $3 1148479
655 7 $a Electronic books. $2 local $3 554714
690 $a 0538
690 $a 0790
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $3 845424
773 0 $t Dissertations Abstracts International $g 85-01B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516428 $z click for full text (PQDT)