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On Applications of Optimization Techniques in UAV Communication, Resource Allocation, And Adversarial Attacks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On Applications of Optimization Techniques in UAV Communication, Resource Allocation, And Adversarial Attacks./
作者:	Rahmati, Ali.
面頁冊數:	1 online resource (186 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Wireless networks. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28747733click for full text (PQDT)
ISBN:	9798494448453

On Applications of Optimization Techniques in UAV Communication, Resource Allocation, And Adversarial Attacks.
Rahmati, Ali.

On Applications of Optimization Techniques in UAV Communication, Resource Allocation, And Adversarial Attacks. - 1 online resource (186 pages)

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2021.

Includes bibliographical references

The explosive development of modern technologies leads to emergence of various interesting trade-offs among different aspects of each system design problem. In this dissertation, we deploy optimization techniques to address some important problems in three emerging areas: UAV-assisted networks, millimeter-wave (mmWave) multiple access UAV networks, and neural networks.In the first part of this dissertation, we focus on UAV-assisted networks in the presence of interference. UAV is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an UAV network. With this consideration, in Chapter 2, an adaptive dynamic interference avoidance scheme is proposed for UAVs coexisting with a primary network. In the proposed scheme, the mobile UAVs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the UAV network. We utilize convex optimization techniques to optimize the transmission powers. We also approach the problem from the intentional interferer's perspective where smart jammers chase the UAVs to effectively degrade the data flow.In the second part of this thesis, we study the energy efficiency of the UAVs in communication networks with realistic channel models in Chapter 3. Since UAVs are power-limited, energy and spectral efficient communication is of paramount importance. To that end, multiple access (MA) schemes can play an important role in achieving high energy efficiency and spectral efficiency. In this part, we introduce rate-splitting MA (RSMA) and non-orthogonal MA (NOMA) schemes in a cellular-connected UAV network. In particular, we investigate the energy efficiency of the RSMA and NOMA schemes in a mmWave downlink transmission scenario. Furthermore, we optimize precoding vectors of both schemes by explicitly taking into account the 3GPP antenna propagation patterns. Moreover, we consider the uplink mmWave transmission between a set of UAVs and a base station (BS), where the UAVs deploy uplink NOMA in multiple clusters. Considering the limited energy budget of UAVs, we formulate an energy efficiency (EE) problem, and propose a solution aided by the Dinkelbach's algorithm and successive convex approximation (SCA). Using realistic air-to-ground (A2G) and terrestrial channel models, we assess the performance of the proposed algorithm under various circumstances (maximum transmit power for UAVs, quality-of-service (QoS) constraint for the desired UE, etc.), and identify the best use cases.In the last part, we investigate the generation of adversarial examples for neural networks in Chapter 4 and Chapter 5. Adversarial examples are known as carefully perturbed data samples fooling deep neural network classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, and each one is responded with the top-1 label of the classifier.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798494448453Subjects--Topical Terms:

1531264
Wireless networks.
Index Terms--Genre/Form:

542853
Electronic books.

On Applications of Optimization Techniques in UAV Communication, Resource Allocation, And Adversarial Attacks.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2021.
504 $a Includes bibliographical references
520 $a The explosive development of modern technologies leads to emergence of various interesting trade-offs among different aspects of each system design problem. In this dissertation, we deploy optimization techniques to address some important problems in three emerging areas: UAV-assisted networks, millimeter-wave (mmWave) multiple access UAV networks, and neural networks.In the first part of this dissertation, we focus on UAV-assisted networks in the presence of interference. UAV is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an UAV network. With this consideration, in Chapter 2, an adaptive dynamic interference avoidance scheme is proposed for UAVs coexisting with a primary network. In the proposed scheme, the mobile UAVs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the UAV network. We utilize convex optimization techniques to optimize the transmission powers. We also approach the problem from the intentional interferer's perspective where smart jammers chase the UAVs to effectively degrade the data flow.In the second part of this thesis, we study the energy efficiency of the UAVs in communication networks with realistic channel models in Chapter 3. Since UAVs are power-limited, energy and spectral efficient communication is of paramount importance. To that end, multiple access (MA) schemes can play an important role in achieving high energy efficiency and spectral efficiency. In this part, we introduce rate-splitting MA (RSMA) and non-orthogonal MA (NOMA) schemes in a cellular-connected UAV network. In particular, we investigate the energy efficiency of the RSMA and NOMA schemes in a mmWave downlink transmission scenario. Furthermore, we optimize precoding vectors of both schemes by explicitly taking into account the 3GPP antenna propagation patterns. Moreover, we consider the uplink mmWave transmission between a set of UAVs and a base station (BS), where the UAVs deploy uplink NOMA in multiple clusters. Considering the limited energy budget of UAVs, we formulate an energy efficiency (EE) problem, and propose a solution aided by the Dinkelbach's algorithm and successive convex approximation (SCA). Using realistic air-to-ground (A2G) and terrestrial channel models, we assess the performance of the proposed algorithm under various circumstances (maximum transmit power for UAVs, quality-of-service (QoS) constraint for the desired UE, etc.), and identify the best use cases.In the last part, we investigate the generation of adversarial examples for neural networks in Chapter 4 and Chapter 5. Adversarial examples are known as carefully perturbed data samples fooling deep neural network classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, and each one is responded with the top-1 label of the classifier.
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