{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/141162"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/141162","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"UAV-Enabled Wireless Communications: Deployment, Optimization, and Analysis","abstract":"Unmanned aerial vehicles (UAVs), known as drones, are a promising solution, as aerial base stations (BSs) or relays, in wireless communications systems. Due to their high likelihood of line-of-sight (LoS) links and ease of deployment, they play a crucial role in providing faster and better wireless network access and service where extra network resources are needed short term, like sports events, or to those emerging services that require high-capacity communications. Moreover, they can help extend wireless coverage to locations deprived of end-to-end wireless communications services in remote/rural areas due to natural disasters or being distant from the conventional terrestrial BSs. However, utilizing this new technology comes with its own novel challenges. In this dissertation, we focus on unprecedented challenges in UAV communications and networks, considering some unique features of UAV networks, such as their optimal placement and wireless backhaul links. First, we focus on provisioning wireless coverage to those emerging services, like extended reality, demanding high-capacity communications. High frequencies, i.e., millimeter-wave (mmWave) and, terahertz frequency bands offer the substantial bandwidth required for such services. These high-frequency communications, however, depend critically on maintaining LoS connections to user terminals. In practical scenarios, users distributed in three-dimensional space often experience severely limited visibility due to environmental obstructions like buildings and foliage. We study the problem of finding an optimal 3D placement and antenna orientation for mmWave-equipped UAVs to minimize the number of required UAVs while maximizing the signal-to-noise ratios (SNRs) to all users. Our approach formulates this as an integer linear programming (ILP) optimization problem, establishes its computational intractability (NP-hardness), and develops a computationally efficient geometric algorithm that consistently achieves near-complete LoS coverage across diverse simulation scenarios. Our second research thrust targets wireless connectivity in remote rural environments—such as agricultural Internet of Things (IoT) deployments—where conventional terrestrial infrastructure is limited or absent. A fundamental challenge in such UAV-assisted networks is determining the minimal UAV deployment that simultaneously achieves two objectives: complete ground user coverage and reliable wireless backhaul connectivity linking all UAVs to terrestrial BSs. We formulate this joint optimization—termed the Backhaul-and-coverage-aware Drone Deployment (BoaRD) problem—as an ILP problem and prove its NP-hardness. Our solution approach employs a graph-theoretic algorithm that efficiently solves the problem with provable performance bounds. Comparative analysis using ILP solvers demonstrates that our algorithm achieves near-optimal performance for smaller problem instances. For large-scale scenarios with extensive coverage areas and numerous users, comprehensive simulations show our algorithm substantially outperforms baseline algorithms while guaranteeing complete user coverage and end-to-end connectivity. Finally, building upon these deployment optimization contributions, our third research thrust develops a comprehensive analytical framework for multi-hop UAV-assisted cellular networks. While the previous work provides deterministic algorithms for specific deployments, understanding system-wide performance requires statistical modeling of networks with random spatial distributions. We develop a comprehensive stochastic geometry framework for analyzing multi-hop UAV-assisted cellular networks that addresses fundamental gaps in existing analytical approaches. Traditional stochastic geometry techniques for terrestrial networks are insufficient for characterizing the complex 3D spatial relationships, interference patterns, and unique propagation characteristics inherent in multi-hop UAV deployments. We extend existing mathematical frameworks to accommodate the distinctive features of aerial networks, including realistic 3D spatial distributions of UAVs across multiple operational altitudes, probabilistic air-to-ground channel models that distinguish between LoS and NLoS conditions, and the intricate interference correlations that arise in multi-hop communication paths. Our framework derives novel mathematical constructs and probability distributions that enable precise characterization of multi-hop network behavior under random spatial deployments in the 3D space. We provide comprehensive closed-form expressions for coverage probability analysis covering both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols, accounting for the hybrid communication scheme where UEs can connect either directly to serving BSs or through the multi-hop UAV network based on received signal quality. Additionally, we introduce optimal relay selection strategies that maximize end-to-end SINR by jointly considering all link qualities in the formed multi-UAV network and accounting for the complex interdependencies between sequential links in the presence of interference. Through extensive theoretical analysis and simulation validation, our results demonstrate that well-designed multi-hop UAV networks can significantly enhance coverage probability and network reliability compared to single-hop architectures, particularly in challenging environments where direct links between UAVs and terrestrial BSs are weak or unavailable due to distance or environmental obstructions.","abstract_html":"Unmanned aerial vehicles (UAVs), known as drones, are a promising solution, as aerial base stations (BSs) or relays, in wireless communications systems. Due to their high likelihood of line-of-sight (LoS) links and ease of deployment, they play a crucial role in providing faster and better wireless network access and service where extra network resources are needed short term, like sports events, or to those emerging services that require high-capacity communications. Moreover, they can help extend wireless coverage to locations deprived of end-to-end wireless communications services in remote/rural areas due to natural disasters or being distant from the conventional terrestrial BSs. However, utilizing this new technology comes with its own novel challenges. In this dissertation, we focus on unprecedented challenges in UAV communications and networks, considering some unique features of UAV networks, such as their optimal placement and wireless backhaul links. First, we focus on provisioning wireless coverage to those emerging services, like extended reality, demanding high-capacity communications. High frequencies, i.e., millimeter-wave (mmWave) and, terahertz frequency bands offer the substantial bandwidth required for such services. These high-frequency communications, however, depend critically on maintaining LoS connections to user terminals. In practical scenarios, users distributed in three-dimensional space often experience severely limited visibility due to environmental obstructions like buildings and foliage. We study the problem of finding an optimal 3D placement and antenna orientation for mmWave-equipped UAVs to minimize the number of required UAVs while maximizing the signal-to-noise ratios (SNRs) to all users. Our approach formulates this as an integer linear programming (ILP) optimization problem, establishes its computational intractability (NP-hardness), and develops a computationally efficient geometric algorithm that consistently achieves near-complete LoS coverage across diverse simulation scenarios. Our second research thrust targets wireless connectivity in remote rural environments—such as agricultural Internet of Things (IoT) deployments—where conventional terrestrial infrastructure is limited or absent. A fundamental challenge in such UAV-assisted networks is determining the minimal UAV deployment that simultaneously achieves two objectives: complete ground user coverage and reliable wireless backhaul connectivity linking all UAVs to terrestrial BSs. We formulate this joint optimization—termed the Backhaul-and-coverage-aware Drone Deployment (BoaRD) problem—as an ILP problem and prove its NP-hardness. Our solution approach employs a graph-theoretic algorithm that efficiently solves the problem with provable performance bounds. Comparative analysis using ILP solvers demonstrates that our algorithm achieves near-optimal performance for smaller problem instances. For large-scale scenarios with extensive coverage areas and numerous users, comprehensive simulations show our algorithm substantially outperforms baseline algorithms while guaranteeing complete user coverage and end-to-end connectivity. Finally, building upon these deployment optimization contributions, our third research thrust develops a comprehensive analytical framework for multi-hop UAV-assisted cellular networks. While the previous work provides deterministic algorithms for specific deployments, understanding system-wide performance requires statistical modeling of networks with random spatial distributions. We develop a comprehensive stochastic geometry framework for analyzing multi-hop UAV-assisted cellular networks that addresses fundamental gaps in existing analytical approaches. Traditional stochastic geometry techniques for terrestrial networks are insufficient for characterizing the complex 3D spatial relationships, interference patterns, and unique propagation characteristics inherent in multi-hop UAV deployments. We extend existing mathematical frameworks to accommodate the distinctive features of aerial networks, including realistic 3D spatial distributions of UAVs across multiple operational altitudes, probabilistic air-to-ground channel models that distinguish between LoS and NLoS conditions, and the intricate interference correlations that arise in multi-hop communication paths. Our framework derives novel mathematical constructs and probability distributions that enable precise characterization of multi-hop network behavior under random spatial deployments in the 3D space. We provide comprehensive closed-form expressions for coverage probability analysis covering both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols, accounting for the hybrid communication scheme where UEs can connect either directly to serving BSs or through the multi-hop UAV network based on received signal quality. Additionally, we introduce optimal relay selection strategies that maximize end-to-end SINR by jointly considering all link qualities in the formed multi-UAV network and accounting for the complex interdependencies between sequential links in the presence of interference. Through extensive theoretical analysis and simulation validation, our results demonstrate that well-designed multi-hop UAV networks can significantly enhance coverage probability and network reliability compared to single-hop architectures, particularly in challenging environments where direct links between UAVs and terrestrial BSs are weak or unavailable due to distance or environmental obstructions.","abstract_has_math":false,"creators":["SabzehAli Touranposhti, Javad"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Reed, Jeffrey H."],"committee_members":["Ha, Dong S.","Dhillon, Harpreet Singh","Asbeck, Alan Thomas","Tripathi, Nishithkumar Dhananjay"],"year":2026,"date_issued":"2026-02-04","date_published":"2026-02-04","updated_at":"2026-07-24T05:56:09Z","subjects":["Unmanned Aerial Vehicles","Multi-UAV Networks","Graph Theory","Approximate Algorithms","Wireless Backhaul","mmWave Communications","Line-of-Sight","Directional Antenna","Optimal Relay Selection","Stochastic Geometry","Amplify-and-Forward","Decode-and-Forward."],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45600"],"render_values":[{"text":"vt_gsexam:45600","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/141162","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Reed, Jeffrey H."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Ha, Dong S.","Dhillon, Harpreet Singh","Asbeck, Alan Thomas","Tripathi, Nishithkumar Dhananjay"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["SabzehAli Touranposhti, Javad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-05T09:00:42Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-05T09:00:42Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-04"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Unmanned Aerial Vehicles","Multi-UAV Networks","Graph Theory","Approximate Algorithms","Wireless Backhaul","mmWave Communications","Line-of-Sight","Directional Antenna","Optimal Relay Selection","Stochastic Geometry","Amplify-and-Forward","Decode-and-Forward."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45600"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/141162"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Unmanned aerial vehicles (UAVs), known as drones, are a promising solution, as aerial base stations (BSs) or relays, in wireless communications systems. Due to their high likelihood of line-of-sight (LoS) links and ease of deployment, they play a crucial role in providing faster and better wireless network access and service where extra network resources are needed short term, like sports events, or to those emerging services that require high-capacity communications. Moreover, they can help extend wireless coverage to locations deprived of end-to-end wireless communications services in remote/rural areas due to natural disasters or being distant from the conventional terrestrial BSs. However, utilizing this new technology comes with its own novel challenges. In this dissertation, we focus on unprecedented challenges in UAV communications and networks, considering some unique features of UAV networks, such as their optimal placement and wireless backhaul links. First, we focus on provisioning wireless coverage to those emerging services, like extended reality, demanding high-capacity communications. High frequencies, i.e., millimeter-wave (mmWave) and, terahertz frequency bands offer the substantial bandwidth required for such services. These high-frequency communications, however, depend critically on maintaining LoS connections to user terminals. In practical scenarios, users distributed in three-dimensional space often experience severely limited visibility due to environmental obstructions like buildings and foliage. We study the problem of finding an optimal 3D placement and antenna orientation for mmWave-equipped UAVs to minimize the number of required UAVs while maximizing the signal-to-noise ratios (SNRs) to all users. Our approach formulates this as an integer linear programming (ILP) optimization problem, establishes its computational intractability (NP-hardness), and develops a computationally efficient geometric algorithm that consistently achieves near-complete LoS coverage across diverse simulation scenarios. Our second research thrust targets wireless connectivity in remote rural environments—such as agricultural Internet of Things (IoT) deployments—where conventional terrestrial infrastructure is limited or absent. A fundamental challenge in such UAV-assisted networks is determining the minimal UAV deployment that simultaneously achieves two objectives: complete ground user coverage and reliable wireless backhaul connectivity linking all UAVs to terrestrial BSs. We formulate this joint optimization—termed the Backhaul-and-coverage-aware Drone Deployment (BoaRD) problem—as an ILP problem and prove its NP-hardness. Our solution approach employs a graph-theoretic algorithm that efficiently solves the problem with provable performance bounds. Comparative analysis using ILP solvers demonstrates that our algorithm achieves near-optimal performance for smaller problem instances. For large-scale scenarios with extensive coverage areas and numerous users, comprehensive simulations show our algorithm substantially outperforms baseline algorithms while guaranteeing complete user coverage and end-to-end connectivity. Finally, building upon these deployment optimization contributions, our third research thrust develops a comprehensive analytical framework for multi-hop UAV-assisted cellular networks. While the previous work provides deterministic algorithms for specific deployments, understanding system-wide performance requires statistical modeling of networks with random spatial distributions. We develop a comprehensive stochastic geometry framework for analyzing multi-hop UAV-assisted cellular networks that addresses fundamental gaps in existing analytical approaches. Traditional stochastic geometry techniques for terrestrial networks are insufficient for characterizing the complex 3D spatial relationships, interference patterns, and unique propagation characteristics inherent in multi-hop UAV deployments. We extend existing mathematical frameworks to accommodate the distinctive features of aerial networks, including realistic 3D spatial distributions of UAVs across multiple operational altitudes, probabilistic air-to-ground channel models that distinguish between LoS and NLoS conditions, and the intricate interference correlations that arise in multi-hop communication paths. Our framework derives novel mathematical constructs and probability distributions that enable precise characterization of multi-hop network behavior under random spatial deployments in the 3D space. We provide comprehensive closed-form expressions for coverage probability analysis covering both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols, accounting for the hybrid communication scheme where UEs can connect either directly to serving BSs or through the multi-hop UAV network based on received signal quality. Additionally, we introduce optimal relay selection strategies that maximize end-to-end SINR by jointly considering all link qualities in the formed multi-UAV network and accounting for the complex interdependencies between sequential links in the presence of interference. Through extensive theoretical analysis and simulation validation, our results demonstrate that well-designed multi-hop UAV networks can significantly enhance coverage probability and network reliability compared to single-hop architectures, particularly in challenging environments where direct links between UAVs and terrestrial BSs are weak or unavailable due to distance or environmental obstructions."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["As wireless communication demands continue to grow, drone-based networks are emerging as a powerful solution to extend cellular coverage and provide high-speed internet access in areas where traditional cell towers are impractical or unavailable. This dissertation addresses three critical challenges in deploying effective drone communication networks. The first challenge involves positioning drones equipped with high-frequency millimeter-wave antennas, which can provide extremely fast internet speeds but require precise line-of-sight connections to users. We developed algorithms that determine the optimal 3D placement and antenna orientation for these drones, ensuring reliable coverage even when users have limited visibility due to surrounding buildings, trees, or other obstacles. Our approach guarantees connectivity to nearly 100% of users in various scenarios, significantly outperforming traditional methods. The second challenge focuses on designing efficient multi-drone networks that minimize costs while ensuring complete coverage. When deploying multiple drones to serve a large area, each drone must not only cover ground users but also maintain wireless connections to other drones, creating a \"backhaul\" network that connects to existing cellular infrastructure. We formulated this as an optimization problem and developed a practical algorithm that reduces the number of required drones by up to 95% compared to random deployment strategies, while guaranteeing both user coverage and drone-to-drone connectivity. The third challenge involves creating mathematical models to analyze and predict the performance of complex multi-hop drone networks, where data may travel through several drones before reaching its destination. Traditional network analysis methods, designed for ground-based systems, are inadequate for three-dimensional drone deployments. We developed a comprehensive analytical framework using advanced mathematical techniques that accounts for the unique characteristics of aerial networks, including 3D spatial distributions, air-to-ground communication channels, and interference between multiple communication paths. This framework enables network designers to evaluate different deployment strategies and optimize system performance before actual implementation. Together, these contributions provide a complete foundation for designing, deploying, and analyzing drone-assisted cellular networks. The research has practical applications in emergency response scenarios, rural connectivity initiatives, temporary event coverage, and next-generation wireless systems. By addressing challenges from individual drone placement to network-wide optimization and theoretical modeling, this work advances the integration of drone technology into modern communication infrastructure, potentially bringing high-speed wireless access to underserved areas and improving network resilience in challenging environments."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["UAV-Enabled Wireless Communications: Deployment, Optimization, and Analysis"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Reed, Jeffrey H."],"dc:contributor.committeemember":["Ha, Dong S.","Dhillon, Harpreet Singh","Asbeck, Alan Thomas","Tripathi, Nishithkumar Dhananjay"],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["SabzehAli Touranposhti, Javad"],"dc:date.accessioned":["2026-02-05T09:00:42Z"],"dc:date.available":["2026-02-05T09:00:42Z"],"dc:date.issued":["2026-02-04"],"dc:description.abstract":["Unmanned aerial vehicles (UAVs), known as drones, are a promising solution, as aerial base stations (BSs) or relays, in wireless communications systems. Due to their high likelihood of line-of-sight (LoS) links and ease of deployment, they play a crucial role in providing faster and better wireless network access and service where extra network resources are needed short term, like sports events, or to those emerging services that require high-capacity communications. Moreover, they can help extend wireless coverage to locations deprived of end-to-end wireless communications services in remote/rural areas due to natural disasters or being distant from the conventional terrestrial BSs. However, utilizing this new technology comes with its own novel challenges. In this dissertation, we focus on unprecedented challenges in UAV communications and networks, considering some unique features of UAV networks, such as their optimal placement and wireless backhaul links. First, we focus on provisioning wireless coverage to those emerging services, like extended reality, demanding high-capacity communications. High frequencies, i.e., millimeter-wave (mmWave) and, terahertz frequency bands offer the substantial bandwidth required for such services. These high-frequency communications, however, depend critically on maintaining LoS connections to user terminals. In practical scenarios, users distributed in three-dimensional space often experience severely limited visibility due to environmental obstructions like buildings and foliage. We study the problem of finding an optimal 3D placement and antenna orientation for mmWave-equipped UAVs to minimize the number of required UAVs while maximizing the signal-to-noise ratios (SNRs) to all users. Our approach formulates this as an integer linear programming (ILP) optimization problem, establishes its computational intractability (NP-hardness), and develops a computationally efficient geometric algorithm that consistently achieves near-complete LoS coverage across diverse simulation scenarios. Our second research thrust targets wireless connectivity in remote rural environments—such as agricultural Internet of Things (IoT) deployments—where conventional terrestrial infrastructure is limited or absent. A fundamental challenge in such UAV-assisted networks is determining the minimal UAV deployment that simultaneously achieves two objectives: complete ground user coverage and reliable wireless backhaul connectivity linking all UAVs to terrestrial BSs. We formulate this joint optimization—termed the Backhaul-and-coverage-aware Drone Deployment (BoaRD) problem—as an ILP problem and prove its NP-hardness. Our solution approach employs a graph-theoretic algorithm that efficiently solves the problem with provable performance bounds. Comparative analysis using ILP solvers demonstrates that our algorithm achieves near-optimal performance for smaller problem instances. For large-scale scenarios with extensive coverage areas and numerous users, comprehensive simulations show our algorithm substantially outperforms baseline algorithms while guaranteeing complete user coverage and end-to-end connectivity. Finally, building upon these deployment optimization contributions, our third research thrust develops a comprehensive analytical framework for multi-hop UAV-assisted cellular networks. While the previous work provides deterministic algorithms for specific deployments, understanding system-wide performance requires statistical modeling of networks with random spatial distributions. We develop a comprehensive stochastic geometry framework for analyzing multi-hop UAV-assisted cellular networks that addresses fundamental gaps in existing analytical approaches. Traditional stochastic geometry techniques for terrestrial networks are insufficient for characterizing the complex 3D spatial relationships, interference patterns, and unique propagation characteristics inherent in multi-hop UAV deployments. We extend existing mathematical frameworks to accommodate the distinctive features of aerial networks, including realistic 3D spatial distributions of UAVs across multiple operational altitudes, probabilistic air-to-ground channel models that distinguish between LoS and NLoS conditions, and the intricate interference correlations that arise in multi-hop communication paths. Our framework derives novel mathematical constructs and probability distributions that enable precise characterization of multi-hop network behavior under random spatial deployments in the 3D space. We provide comprehensive closed-form expressions for coverage probability analysis covering both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols, accounting for the hybrid communication scheme where UEs can connect either directly to serving BSs or through the multi-hop UAV network based on received signal quality. Additionally, we introduce optimal relay selection strategies that maximize end-to-end SINR by jointly considering all link qualities in the formed multi-UAV network and accounting for the complex interdependencies between sequential links in the presence of interference. Through extensive theoretical analysis and simulation validation, our results demonstrate that well-designed multi-hop UAV networks can significantly enhance coverage probability and network reliability compared to single-hop architectures, particularly in challenging environments where direct links between UAVs and terrestrial BSs are weak or unavailable due to distance or environmental obstructions."],"dc:description.abstractgeneral":["As wireless communication demands continue to grow, drone-based networks are emerging as a powerful solution to extend cellular coverage and provide high-speed internet access in areas where traditional cell towers are impractical or unavailable. This dissertation addresses three critical challenges in deploying effective drone communication networks. The first challenge involves positioning drones equipped with high-frequency millimeter-wave antennas, which can provide extremely fast internet speeds but require precise line-of-sight connections to users. We developed algorithms that determine the optimal 3D placement and antenna orientation for these drones, ensuring reliable coverage even when users have limited visibility due to surrounding buildings, trees, or other obstacles. Our approach guarantees connectivity to nearly 100% of users in various scenarios, significantly outperforming traditional methods. The second challenge focuses on designing efficient multi-drone networks that minimize costs while ensuring complete coverage. When deploying multiple drones to serve a large area, each drone must not only cover ground users but also maintain wireless connections to other drones, creating a \"backhaul\" network that connects to existing cellular infrastructure. We formulated this as an optimization problem and developed a practical algorithm that reduces the number of required drones by up to 95% compared to random deployment strategies, while guaranteeing both user coverage and drone-to-drone connectivity. The third challenge involves creating mathematical models to analyze and predict the performance of complex multi-hop drone networks, where data may travel through several drones before reaching its destination. Traditional network analysis methods, designed for ground-based systems, are inadequate for three-dimensional drone deployments. We developed a comprehensive analytical framework using advanced mathematical techniques that accounts for the unique characteristics of aerial networks, including 3D spatial distributions, air-to-ground communication channels, and interference between multiple communication paths. This framework enables network designers to evaluate different deployment strategies and optimize system performance before actual implementation. Together, these contributions provide a complete foundation for designing, deploying, and analyzing drone-assisted cellular networks. The research has practical applications in emergency response scenarios, rural connectivity initiatives, temporary event coverage, and next-generation wireless systems. By addressing challenges from individual drone placement to network-wide optimization and theoretical modeling, this work advances the integration of drone technology into modern communication infrastructure, potentially bringing high-speed wireless access to underserved areas and improving network resilience in challenging environments."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45600"],"dc:identifier.uri":["https://hdl.handle.net/10919/141162"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Unmanned Aerial Vehicles","Multi-UAV Networks","Graph Theory","Approximate Algorithms","Wireless Backhaul","mmWave Communications","Line-of-Sight","Directional Antenna","Optimal Relay Selection","Stochastic Geometry","Amplify-and-Forward","Decode-and-Forward."],"dc:title":["UAV-Enabled Wireless Communications: Deployment, Optimization, and Analysis"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-24T05:56:09Z"}