Robust Resource Allocation for UAV Systems with UAV Jittering and User Location Uncertainty

Xu, Dongfang; Sun, Yan; Ng, Derrick Wing Kwan; Schober, Robert

doi:10.1109/glocomw.2018.8644086

Cited by 34 publications

(25 citation statements)

References 21 publications

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“…, v (1) and the vertex set of P (1) is denoted as T (1) = ν (1) . Based on vertex ν (1) , we generate K + 6 new vertices in the vertex set…”

Section: Optimal Algorithm Designmentioning

confidence: 99%

“…Unmanned aerial vehicle (UAV) based wireless communication systems have received considerable attention as a promising approach for offering real-time high data-rate communication services [1]- [7]. Compared to conventional cellular systems relying on a fixed terrestrial infrastructure, UAV-assisted communication systems can provide on-demand connectivity by flexibly deploying UAV-mounted wireless transceivers over a target area.…”

Section: Introductionmentioning

confidence: 99%

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Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

Sun

et al. 2020

IEEE Trans. Commun.

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174

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In this paper, we investigate robust resource allocation algorithm design for multiuser downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication systems, where we account for the various uncertainties that are unavoidable in such systems and, if left unattended, may severely degrade system performance.We jointly optimize the two-dimensional (2-D) trajectory and the transmit beamforming vector of the UAV for minimization of the total power consumption. The algorithm design is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the angle of departure (AoD) caused by UAV jittering, user location uncertainty, wind speed uncertainty, and polygonal no-fly zones (NFZs). Despite the non-convexity of the optimization problem, we solve it optimally by employing monotonic optimization theory and semidefinite programming relaxation which yields the optimal 2-D trajectory and beamforming policy. Since the developed optimal resource allocation algorithm entails a high computational complexity, we also propose a suboptimal iterative low-complexity scheme based on successive convex approximation to strike a balance between optimality and computational complexity. Our simulation results reveal not only the significant power savings enabled by the proposed algorithms compared to two baseline schemes, but also confirm their robustness with respect to UAV jittering, wind speed uncertainty, and user location uncertainty. Moreover, our results unveil that the joint presence of wind speed uncertainty and NFZs has a considerable impact on the UAV trajectory. Nevertheless, by counteracting the wind speed uncertainty with the proposed robust design, we can simultaneously minimize the total UAV power consumption and ensure a secure trajectory that does not trespass any NFZ. ). This paper was presented in part at IEEE Globecom 2018 [1]. 2 be employed as aerial base stations to offer temporary communication links in a timely and cost-effective manner. Moreover, due to their high mobility and maneuverability, UAVs can adapt their trajectories based on the actual environment and terrain which improves system performance [3]. As a result, UAV-assisted communication systems have drawn significant attention from both academia and industry. For instance, the authors of [4] studied suboptimal UAV trajectory design for maximization of the energy-efficiency of UAV communication systems. The authors of [5] proposed a suboptimal joint trajectory, power allocation, and user scheduling algorithm for maximization of the minimum user throughput in multi-UAV systems.Secure UAV communications was investigated in [6] where the trajectory of a UAV and its transmit power were jointly optimized to maximize the system secrecy rate. The authors of [7] proposed solar-powered UAV communication systems and studied the jointly optimal resource allocation and UAV trajectory design for maximization of the system sum throughput. In fact, the throughput of UAV communication systems can be further i...

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“…, v (1) and the vertex set of P (1) is denoted as T (1) = ν (1) . Based on vertex ν (1) , we generate K + 6 new vertices in the vertex set…”

Section: Optimal Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

Sun

et al. 2020

IEEE Trans. Commun.

Self Cite

174

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show abstract

“…The authors discuss the challneges, future research directions, and statistical methods for analyzing and improving their performance. They also have presented their study results, [40], on energy consumption efficiency regarding optimal localization and number of deployed drones.…”

Section: ) Uavs As Bssmentioning

confidence: 99%

Potential Data Link Candidates for Civilian Unmanned Aircraft Systems: A Survey

Zolanvari

Jain

Salman

2020

IEEE Commun. Surv. Tutorials

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This survey studies the potential data link candidates for unmanned aircraft vehicles (UAVs). There has been tremendous growth in different applications of UAVs such as lifesaving and rescue missions, commercial use, recreations, etc. Unlike the traditional wireless communications, the data links for these systems do not have any general standardized framework yet to ensure safe co-existence of UAVs with other flying vehicles. This motivated us to provide a comprehensive survey of potential data link technologies available for UAVs. Our goal is to study the current trends and available candidates and carry out a comprehensive comparison among them. The contribution of this survey is to highlight the strength and weakness of the current data link options and their suitability to satisfy the UAVs communication requirements. Satellite links, cellular technologies, Wi-Fi and several similar wireless technologies are studied thoroughly in this paper. We also focus on several available promising standards that can be modified for these data links. Then, we discuss standard-related organizations that are working actively in the area of civilian unmanned systems. Finally, we bring up some future challenges in this area with several potential solutions to motivate further research work. Index Terms-Unmanned aircraft systems (UAS), unmanned aircraft vehicle (UAV), civilian applications, data link, satellite communication, cellular communication, aviation standards, standard bodies, future challenges. SATCOM Provider ATC Pilot Cellular Provider Satellite UAV Cellular Tower Fig. 1 2 2) Network RequirementsThere are several survey papers discussing the requirements for UAS data link such as [2], and [9][10][11][12][13][14]. For example, the requirements for the UASs based on four different areas of missions, communication networks, communication data links, navigation, and surveillance are discussed in [2]. Several detailed discussions about the communication data link requirements such as range, velocity, latency, reliability, availability, integrity, security, and bit rate are provided. As another instance, a survey of UAVs communication requirements has been studied in [9]. These requirements are based on four categories of UAV applications (i.e., search and rescue, area coverage, delivery/transportation, and construction). Moreover, the important requirements and considerations regarding the network layer of UAV communications have been discussed in [14]. 3) Channel ModelingChannel modeling must be studied for designing and evaluating proper UAV data links for different applications. There have been several research work and surveys on channel modeling for UAV communications, such as [15][16][17][18]. For instance, a comprehensive survey on channel modeling for UAV communications is provided in [15]. Different channel modeling approaches along with the associated challenges have been studied. 4) Our ContributionWe have studied the existing survey papers on the aforementioned issues related to the UAV data links. In this s...

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“…The authors considered the problem associated with the user association and content replacement for resource allocations. Xu et al [9] focused on the resource allocation problems for UAVs based on a non-convex optimization problem for total transmit power of a downlink.…”

Section: Related Workmentioning

confidence: 99%

Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)

Sharma¹,

You²,

Seo³

et al. 2019

IEEE Access

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Aerial-terrestrial cloud networks (ATCNs), global integration of air and ground communication systems, pave a way for a large set of applications such as surveillance, on-demand transmissions, dataacquisition, and navigation. However, such networks suffer from crucial challenges of secure and reliable resource allocation and content-caching as the involved entities are highly dynamic and there is no finetuned strategy to accommodate their connectivity. To resolve this quandary, cog-chain, a novel paradigm for secure and reliable resource allocation and content-caching in ATCNs, is presented. Various requirements, key concepts, and issues with ATCNs are also presented along with basic concepts to establish a cogchain in ATCNs. Feed and fetch modes are utilized depending on the involved entities and caching servers. In addition, a cog-chain communication protocol is presented which avails to evaluate the formation of a virtual cog-chain between the nodes and the content-caching servers. The efficacy of the proposed solution is demonstrated through consequential gains observed for signaling overheads, computational time, reliability, and resource allocation growth. The proposed approach operates with the signaling overheads ranging between 30.36 and 303.6 bytes*hops/sec and the formation time between 186 and 195 ms. Furthermore, the overall time consumption is 83.33% lower than the sequential-verification model and the resource allocation growth is 27.17% better than the sequential-verification model. INDEX TERMS Security, reliability, content caching, ATCNs, STINs, cog-chain.

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Robust Resource Allocation for UAV Systems with UAV Jittering and User Location Uncertainty

Cited by 34 publications

References 21 publications

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

Potential Data Link Candidates for Civilian Unmanned Aircraft Systems: A Survey

Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)

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