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Journal articles on the topic 'UAV-aided communications'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Lee, Hoon, Subin Eom, Junhee Park, and Inkyu Lee. "UAV-Aided Secure Communications With Cooperative Jamming." IEEE Transactions on Vehicular Technology 67, no. 10 (October 2018): 9385–92. http://dx.doi.org/10.1109/tvt.2018.2853723.

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2

Xi, Xing, Xianbin Cao, Peng Yang, Jingxuan Chen, Tony Quek, and Dapeng Wu. "Joint User Association and UAV Location Optimization for UAV-Aided Communications." IEEE Wireless Communications Letters 8, no. 6 (December 2019): 1688–91. http://dx.doi.org/10.1109/lwc.2019.2937077.

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3

Feng, Wanmei, Jie Tang, Nan Zhao, Yuli Fu, Xiuyin Zhang, Kanapathippillai Cumanan, and Kai-Kit Wong. "NOMA-based UAV-aided networks for emergency communications." China Communications 17, no. 11 (November 2020): 54–66. http://dx.doi.org/10.23919/jcc.2020.11.005.

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4

Han, Rui, Lin Bai, Yongqing Wen, Jianwei Liu, Jinho Choi, and Wei Zhang. "UAV-Aided Backscatter Communications: Performance Analysis and Trajectory Optimization." IEEE Journal on Selected Areas in Communications 39, no. 10 (October 2021): 3129–43. http://dx.doi.org/10.1109/jsac.2021.3088676.

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5

Wang, Lu, Yue Ling Che, Jinfeng Long, Lingjie Duan, and Kaishun Wu. "Multiple Access MmWave Design for UAV-Aided 5G Communications." IEEE Wireless Communications 26, no. 1 (February 2019): 64–71. http://dx.doi.org/10.1109/mwc.2018.1800216.

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6

Feng, Wei, Jingchao Wang, Yunfei Chen, Xuanxuan Wang, Ning Ge, and Jianhua Lu. "UAV-Aided MIMO Communications for 5G Internet of Things." IEEE Internet of Things Journal 6, no. 2 (April 2019): 1731–40. http://dx.doi.org/10.1109/jiot.2018.2874531.

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7

Fawaz, Wissam, Chadi Abou-Rjeily, and Chadi Assi. "UAV-Aided Cooperation for FSO Communication Systems." IEEE Communications Magazine 56, no. 1 (January 2018): 70–75. http://dx.doi.org/10.1109/mcom.2017.1700320.

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8

Zuo, Xingxuan, Lingfeng Shen, Gangtao Han, and Xiaomin Mu. "Optimization of UAV-Aided Millimeter-Wave IoT Systems." Electronics 10, no. 21 (October 26, 2021): 2618. http://dx.doi.org/10.3390/electronics10212618.

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Due to their maneuverability, unmanned aerial vehicles (UAVs) have grown into a promising enabler of the Internet of Things (IoTs). In addition to the benefits of the bandwidth and communication quality of millimeter-wave (mmWave) systems, a UAV-aided mmWave multiple-input and multiple-output (MIMO) communication system is investigated in this paper for the data collection of IoT systems, in which single-antenna IoT devices are divided into several clusters, and the UAV aided mmWave base station (UAV-BS) collects data from each cluster using the time division scheme. The joint optimization of the beam selection, UAV trajectory, user clustering, power allocation and transmission duration is studied in this paper to improve the data collection efficiency. The solution of the problem is then given in three steps. Firstly, the incremental K-means clustering and ant colony optimization algorithm are utilized to handle the UAV trajectory planning and user clustering problem. Secondly, an incremental beam selection scheme is employed to ensure that all the devices in each cluster can communicate with the UAV. Thirdly, an iterative algorithm is proposed by alternately optimizing the power allocation and transmission duration of the IoT devices. Finally, the simulation results demonstrate the effectiveness of the proposed solution for the UAV-aided mmWave communication system.

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9

Mayor, Vicente, Rafael Estepa, Antonio Estepa, and German Madinabeitia. "Deploying a Reliable UAV-Aided Communication Service in Disaster Areas." Wireless Communications and Mobile Computing 2019 (April 8, 2019): 1–20. http://dx.doi.org/10.1155/2019/7521513.

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When telecommunication infrastructure is damaged by natural disasters, creating a network that can handle voice channels can be vital for search and rescue missions. Unmanned Aerial Vehicles (UAV) equipped with WiFi access points could be rapidly deployed to provide wireless coverage to ground users. This WiFi access network can in turn be used to provide a reliable communication service to be used in search and rescue missions. We formulate a new problem for UAVs optimal deployment which considers not only WiFi coverage but also the mac sublayer (i.e., quality of service). Our goal is to dispatch the minimum number of UAVs for provisioning a WiFi network that enables reliable VoIP communications in disaster scenarios. Among valid solutions, we choose the one that minimizes energy expenditure at the user’s WiFi interface card in order to extend ground user’s smartphone battery life as much as possible. Solutions are found using well-known heuristics such as K-means clusterization and genetic algorithms. Via numerical results, we show that the IEEE 802.11 standard revision has a decisive impact on the number of UAVs required to cover large areas, and that the user’s average energy expenditure (attributable to communications) can be reduced by limiting the maximum altitude for drones or by increasing the VoIP speech quality.

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10

Cao, Dongju, Wendong Yang, Hui Chen, Yang Wu, and Xuanxuan Tang. "Energy efficiency maximization for buffer-aided multi-UAV relaying communications." Journal of Systems Engineering and Electronics 33, no. 2 (April 2022): 312–21. http://dx.doi.org/10.23919/jsee.2022.000032.

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11

Zheng, Jiakang, Jiayi Zhang, and Bo Ai. "UAV Communications With WPT-Aided Cell-Free Massive MIMO Systems." IEEE Journal on Selected Areas in Communications 39, no. 10 (October 2021): 3114–28. http://dx.doi.org/10.1109/jsac.2021.3088632.

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12

Iacovelli, Giovanni, Angelo Coluccia, and Luigi Alfredo Grieco. "Channel Gain Lower Bound for IRS-Assisted UAV-Aided Communications." IEEE Communications Letters 25, no. 12 (December 2021): 3805–9. http://dx.doi.org/10.1109/lcomm.2021.3119239.

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13

Lu, Xiaozhen, Liang Xiao, Canhuang Dai, and Huaiyu Dai. "UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming." IEEE Wireless Communications 27, no. 4 (August 2020): 48–53. http://dx.doi.org/10.1109/mwc.001.1900207.

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14

Yin, Sixing, Shuo Zhao, Yifei Zhao, and F. Richard Yu. "Intelligent Trajectory Design in UAV-Aided Communications With Reinforcement Learning." IEEE Transactions on Vehicular Technology 68, no. 8 (August 2019): 8227–31. http://dx.doi.org/10.1109/tvt.2019.2923214.

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15

Guo, Wei, Weile Zhang, Yongchao Wang, Nan Zhao, and F. Richard Yu. "Joint Attitude and Power Optimization for UAV-Aided Downlink Communications." IEEE Transactions on Vehicular Technology 68, no. 12 (December 2019): 12437–42. http://dx.doi.org/10.1109/tvt.2019.2948762.

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16

Ullah, Zaib, Fadi Al-Turjman, and Leonardo Mostarda. "Cognition in UAV-Aided 5G and Beyond Communications: A Survey." IEEE Transactions on Cognitive Communications and Networking 6, no. 3 (September 2020): 872–91. http://dx.doi.org/10.1109/tccn.2020.2968311.

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17

Lu, Haiquan, Haiyang Zhang, Haibo Dai, Wei Wu, and Baoyun Wang. "Proactive Eavesdropping in UAV-Aided Suspicious Communication Systems." IEEE Transactions on Vehicular Technology 68, no. 2 (February 2019): 1993–97. http://dx.doi.org/10.1109/tvt.2018.2889397.

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18

Zhang, Mingze, Mohammed EI-Hajjar, and Soon Xin Ng. "Intelligent Caching in UAV-Aided Networks." IEEE Transactions on Vehicular Technology 71, no. 1 (January 2022): 739–52. http://dx.doi.org/10.1109/tvt.2021.3125396.

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19

Chen, Chengbin, Jin Xiang, Zhuoya Ye, Wanyi Yan, Suiling Wang, Zhensheng Wang, Pingping Chen, and Min Xiao. "Deep Learning-Based Energy Optimization for Edge Device in UAV-Aided Communications." Drones 6, no. 6 (June 3, 2022): 139. http://dx.doi.org/10.3390/drones6060139.

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Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically adjust the emission energy because the dynamic UAV coordinates cannot be accurately acquired. In addition, the fixed emission energy makes the EDs have poor endurance. To address this challenge, in this paper, we propose a deep learning-based energy optimization algorithm (DEO) to dynamically adjust the emission energy of the ED so that the received energy of the mobile relay UAV is, as much as possible, equal to the sensitivity of the receiver. Specifically, the edge server provides the computing platform and uses deep learning (DL) to predict the location information of the relay UAV in dynamic scenarios. Then, the ED emission energy is adjusted according to the predicted position. It enables the ED to communicate reliably with the mobile relay UAV at minimum energy. We analyze the performance of a variety of predictive networks under different time-delay systems through experiments. The results show that the Weighted Mean Absolute Percentage Error (WMAPE) of this algorithm is 0.54%, 0.80% and 1.15% under the effect of a communication delay of 0.4 s, 0.6 s and 0.8 s, respectively.

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20

Liu, Yuanpeng, Yunlong Wang, Xiao Shen, Jian Wang, and Yuan Shen. "UAV-Aided Relative Localization of Terminals." IEEE Internet of Things Journal 8, no. 16 (August 15, 2021): 12999–3013. http://dx.doi.org/10.1109/jiot.2021.3064216.

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21

Hu, Jie, Xingpeng Cai, and Kun Yang. "Joint Trajectory and Scheduling Design for UAV Aided Secure Backscatter Communications." IEEE Wireless Communications Letters 9, no. 12 (December 2020): 2168–72. http://dx.doi.org/10.1109/lwc.2020.3016174.

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22

Lyu, Jiangbin, Yong Zeng, and Rui Zhang. "Cyclical Multiple Access in UAV-Aided Communications: A Throughput-Delay Tradeoff." IEEE Wireless Communications Letters 5, no. 6 (December 2016): 600–603. http://dx.doi.org/10.1109/lwc.2016.2604306.

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23

Eom, Subin, Hoon Lee, Junhee Park, and Inkyu Lee. "UAV-Aided Wireless Communication Designs With Propulsion Energy Limitations." IEEE Transactions on Vehicular Technology 69, no. 1 (January 2020): 651–62. http://dx.doi.org/10.1109/tvt.2019.2952883.

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24

Tang, Tao, Tao Hong, Haohui Hong, Senyuan Ji, Shahid Mumtaz, and Mohamed Cheriet. "An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios." Electronics 8, no. 10 (October 18, 2019): 1188. http://dx.doi.org/10.3390/electronics8101188.

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The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.

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25

Alsolai, Hadeel, Jaber S. Alzahrani, Mohammed Maray, Mohammed Alghamdi, Ayman Qahmash, Mrim M. Alnfiai, Amira Sayed A. Aziz, and Anwer Mustafa Hilal. "Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-Assisted Intelligent Vehicular Network." Drones 6, no. 11 (November 16, 2022): 358. http://dx.doi.org/10.3390/drones6110358.

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The increasing demands of several emergent services brought new communication problems to vehicular networks (VNs). It is predicted that the transmission system assimilated with unmanned aerial vehicles (UAVs) fulfills the requirement of next-generation vehicular network. Because of its higher flexible mobility, the UAV-aided vehicular network brings transformative and far-reaching benefits with extremely high data rates; considerably improved security and reliability; massive and hyper-fast wireless access; much greener, smarter, and longer 3D communications coverage. The clustering technique in UAV-aided VN is a difficult process because of the limited energy of UAVs, higher mobility, unstable links, and dynamic topology. Therefore, this study introduced an Enhanced Artificial Gorilla Troops Optimizer–based Clustering Protocol for a UAV-Assisted Intelligent Vehicular Network (EAGTOC-UIVN). The goal of the EAGTOC-UIVN technique lies in the clustering of the nodes in UAV-based VN to achieve maximum lifetime and energy efficiency. In the presented EAGTOC-UIVN technique, the EAGTO algorithm was primarily designed by the use of the circle chaotic mapping technique. Moreover, the EAGTOC-UIVN technique computes a fitness function with the inclusion of multiple parameters. To depict the improved performance of the EAGTOC-UIVN technique, a widespread simulation analysis was performed. The comparison study demonstrated the enhancements of the EAGTOC-UIVN technique over other recent approaches.

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26

Wang, Rui, Yong Cao, Adeeb Noor, Thamer A. Alamoudi, and Redhwan Nour. "Agent-enabled task offloading in UAV-aided mobile edge computing." Computer Communications 149 (January 2020): 324–31. http://dx.doi.org/10.1016/j.comcom.2019.10.021.

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27

Wang, Liang, Kezhi Wang, Cunhua Pan, Xiaomin Chen, and Nauman Aslam. "Deep Q-Network Based Dynamic Trajectory Design for UAV-Aided Emergency Communications." Journal of Communications and Information Networks 5, no. 4 (December 2020): 393–402. http://dx.doi.org/10.23919/jcin.2020.9306013.

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28

Li, An, and Wenjing Zhang. "Mobile jammer-aided secure UAV communications via trajectory design and power control." China Communications 15, no. 8 (August 2018): 141–51. http://dx.doi.org/10.1109/cc.2018.8438280.

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29

Peng, Gaozhao, Yongxiang Xia, Xuejun Zhang, and Lin Bai. "UAV-Aided Networks for Emergency Communications in Areas with Unevenly Distributed Users." Journal of Communications and Information Networks 3, no. 4 (December 2018): 23–32. http://dx.doi.org/10.1007/s41650-018-0034-1.

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30

Li, Rong, Yue Xiao, Ping Yang, Wanbin Tang, Mingming Wu, and Yulan Gao. "UAV-Aided Two-Way Relaying for Wireless Communications of Intelligent Robot Swarms." IEEE Access 8 (2020): 56141–50. http://dx.doi.org/10.1109/access.2020.2979478.

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31

Shen, Lingfeng, Ning Wang, Xiang Ji, Xiaomin Mu, and Lin Cai. "Iterative Trajectory Optimization for Physical-Layer Secure Buffer-Aided UAV Mobile Relaying." Sensors 19, no. 15 (August 6, 2019): 3442. http://dx.doi.org/10.3390/s19153442.

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With the fast development of commercial unmanned aerial vehicle (UAV) technology, there are increasing research interests on UAV communications. In this work, the mobility and deployment flexibility of UAVs are exploited to form a buffer-aided relaying system assisting terrestrial communication that is blocked. Optimal UAV trajectory design of the UAV-enabled mobile relaying system with a randomly located eavesdropper is investigated from the physical-layer security perspective to improve the overall secrecy rate. Based on the mobility of the UAV relay, a wireless channel model that changes with the trajectory and is exploited for improved secrecy is established. The secrecy rate is maximized by optimizing the discretized trajectory anchor points based on the information causality and UAV mobility constraints. However, the problem is non-convex and therefore difficult to solve. To make the problem tractable, we alternatively optimize the increments of the trajectory anchor points iteratively in a two-dimensional space and decompose the problem into progressive convex approximate problems through the iterative procedure. Convergence of the proposed iterative trajectory optimization technique is proved analytically by the squeeze principle. Simulation results show that finding the optimal trajectory by iteratively updating the displacements is effective and fast converging. It is also shown by the simulation results that the distribution of the eavesdropper location influences the security performance of the system. Specifically, an eavesdropper further away from the destination is beneficial to the system’s overall secrecy rate. Furthermore, it is observed that eavesdropper being further away from the destination also results in shorter trajectories, which implies it being energy-efficient as well.

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32

Di, Xiaofei, and Yang Chen. "Joint Position and Time Allocation Optimization of UAV-Aided Wireless Powered Relay Communication Systems." Wireless Communications and Mobile Computing 2021 (April 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5537517.

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The Internet of things (IoT) has emerged as a platform for connecting massive physical devices to collect and analyze data for decision-making. Wireless devices in IoT are usually energy-constrained and thus need to be powered by a stable and reliable energy source in order to maintain a long network lifetime. An unmanned aerial vehicle (UAV) as an energy source is a proper and applicable way to supply energy to wireless devices in IoT, due to its flexibility and potential of providing line-of-sight (LOS) links for wireless air-to-ground channels. In this paper, a UAV-aided wireless powered relay communication system is presented, where a UAV firstly emits energy to a source and a relay, and then, the source and relay cooperatively transmit information to their destination. To explore the performance limit of the system, a problem is formulated by jointly optimizing the position of the UAV and time allocation to maximize the achievable information rate of the system. By deriving the explicit expressions of the optimal position of UAV and optimal time fraction, the nonconvex optimization problem is efficiently solved. Simulation results show that our proposed method significantly outperforms the benchmark methods.

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33

Wang, Gicheol, Byoung-Sun Lee, Jae Young Ahn, and Gihwan Cho. "A UAV-Aided Cluster Head Election Framework and Applying Such to Security-Driven Cluster Head Election Schemes: A Survey." Security and Communication Networks 2018 (June 19, 2018): 1–17. http://dx.doi.org/10.1155/2018/6475927.

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UAS (Unmanned Aerial Systems) are now drawing a lot of attention from academic and research fields as well as the general public. The UAS is expected to provide many promising applications such as intelligent transportation system, disaster management, search and rescue, public safety, smart delivery, wild species monitoring, and wireless service area extension. More specifically, as a part of the wireless service extension, we deal with the information dissemination and collection using a UAV in this paper. In this application, because the UAV communicates with each CH (Cluster Head) to collect data from sensor nodes or to disseminate information to the sensor nodes, well-behaved and qualified nodes should be elected as CHs and their integrity should be preserved. Even though a UAV makes the information dissemination and collection process efficient in a WSN, we can make the UAV help the election of new CHs to mitigate the threat of compromised CHs. To this aim, we first propose a UAV-aided CH election framework where a UAV delivers the critical information collected from sensors to the sink, and the sink reselects a set of well-behaved and qualified CHs considering the information. Then, we classify the existing security-driven CH election schemes into several categories and explain the principle of each category and its representative schemes. For each representative scheme, we also explain how to adapt it into the UAV-aided CH election framework. Next, we identify some desirable security properties that a CH election scheme should provide and reveal the security level that each representative scheme reaches for the desirable security properties. Next, we compare communication and computation overhead of the security-driven CH election schemes in terms of the big O notation. In conclusion, we reveal what we have learned from this survey work and provide a future work item.

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34

Yu, Ye, Xiangyuan Bu, Kai Yang, Hongyuan Yang, Xiaozheng Gao, and Zhu Han. "UAV-Aided Low Latency Multi-Access Edge Computing." IEEE Transactions on Vehicular Technology 70, no. 5 (May 2021): 4955–67. http://dx.doi.org/10.1109/tvt.2021.3072065.

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35

Mamaghani, Milad Tatar, and Yi Hong. "Joint Trajectory and Power Allocation Design for Secure Artificial Noise Aided UAV Communications." IEEE Transactions on Vehicular Technology 70, no. 3 (March 2021): 2850–55. http://dx.doi.org/10.1109/tvt.2021.3057397.

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36

Pan, Xi, Chaoxing Yan, and Jiankang Zhang. "Nonlinearity-Based Single-Channel Monopulse Tracking Method for OFDM-Aided UAV A2G Communications." IEEE Access 7 (2019): 148485–94. http://dx.doi.org/10.1109/access.2019.2946960.

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37

Liu, Miao, Guan Gui, Nan Zhao, Jinlong Sun, Haris Gacanin, and Hikmet Sari. "UAV-Aided Air-to-Ground Cooperative Nonorthogonal Multiple Access." IEEE Internet of Things Journal 7, no. 4 (April 2020): 2704–15. http://dx.doi.org/10.1109/jiot.2019.2957225.

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38

Wang, Changyu, Weili Yu, Fusheng Zhu, Jiangtao Ou, Chengyuan Fan, Jianghong Ou, and Dahua Fan. "UAV-Aided Multiuser Mobile Edge Computing Networks with Energy Harvesting." Wireless Communications and Mobile Computing 2022 (June 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6723403.

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This article studies a mobile edge computing (MEC) with one edge node (EN), where multiple unmanned aerial vehicles (UAVs) act as users which have some heavy tasks. As the users generally have limitations in both calculating and power supply, the EN can help calculate the tasks and meanwhile supply the power to the users through energy harvesting. We optimize the system by proposing a joint strategy to unpacking and energy harvesting. Specifically, a deep reinforcement learning (DRL) algorithm is implemented to provide a solution to the unpacking, while several analytical solutions are given to the power allocation of energy harvesting among multiple users. In particular, criterion I is the equivalent power allocation, criterion II is designed through equal data rate, while criterion III is based on the equivalent transmission delay. We finally give some results to verify the joint strategy for the UAV-aided multiuser MEC system with energy harvesting.

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39

Shah, A. F. M. Shahen. "Architecture of Emergency Communication Systems in Disasters through UAVs in 5G and Beyond." Drones 7, no. 1 (December 29, 2022): 25. http://dx.doi.org/10.3390/drones7010025.

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Unmanned aerial vehicles (UAVs) are valued in 5G and 6G networks due to their communication capabilities, low cost, and flexible deployment. Recently, UAV-aided emergency networks in disasters have been designed where one single large UAV is used. Compared with a single large UAV, Flying Ad Hoc Networks (FANETs) with small UAVs have many benefits. Therefore, instead of a single large UAV, a FANET is proposed in this paper. To take full advantage of their services, UAVs must be able to communicate efficiently with each other and with existing networking infrastructures. However, high node mobility is one of the main characteristics of FANETs, which can result in rapid topology changes with frequent link breakage and unstable communications that cause collision and packet loss. As an alternative, networks can be broken up into smaller groups or clusters to control their topology efficiently and reduce channel contention. In this study, a novel cluster-based mechanism is proposed for FANETs. The process of cluster management is described. The IEEE 802.11 backoff method is specifically intended for direct communications and is not appropriate for cluster-based communications. Therefore, a new backoff mechanism is proposed based on cluster size to optimize performance. An analytical study using the Markov chain model is presented to explore the performance of the proposed mechanism. The study takes into account Nakagami-m fading channels. Performance-influencing parameters are taken into consideration and the relationships among these parameters as well as performance metrics such as throughput, packet dropping rate, outage probability, and delay are obtained. Furthermore, simulation results are provided which verify the analytical studies. A quantitative comparison with current cluster-based methods is also presented. The simulation results show that the suggested technique enhances system performance and complies with the safety message delay constraint of 100 ms.

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40

Jun Liu, Yuwei Zhang, Jing Wang, Tao Cui, Lin Zhang, Chao Li, Kai Chen, et al. "Outage Probability Analysis for UAV-Aided Mobile Edge Computing Networks." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 9, no. 31 (June 8, 2022): e4. http://dx.doi.org/10.4108/eetinis.v9i31.960.

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This paper studies one typical mobile edge computing (MEC) system, where a single user has some intensively calculating tasks to be computed by M edge nodes (ENs) with much more powerful calculating capability. In particular, unmanned aerial vehicle (UAV) can act as the ENs due to its flexibility and high mobility in the deployment. For this system, we propose several EN selection criteria to improve the system whole performance of computation and communication. Specifically, criterion I selects the best EN based on maximizing the received signal-to-noise ratio (SNR) at the EN, criterion II performs the selection according to the most powerful calculating capability, while criterion III chooses one EN randomly. For each EN selection criterion, we perform the system performance evaluation by analyzing outage probability (OP) through deriving some analytical expressions. From these expressions, we can obtain some meaningful insights regarding how to design the MEC system. We finally perform some simulation results to demonstrate the effectiveness of the proposed MEC network. In particular, criterion I can exploit the full diversity order equal to M.

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41

Gopi, Sudheesh Puthenveettil, and Maurizio Magarini. "Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization." Electronics 10, no. 23 (November 27, 2021): 2953. http://dx.doi.org/10.3390/electronics10232953.

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The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

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42

Liu, Yuan, Ke Xiong, Yang Lu, Qiang Ni, Pingyi Fan, and Khaled Ben Letaief. "UAV-Aided Wireless Power Transfer and Data Collection in Rician Fading." IEEE Journal on Selected Areas in Communications 39, no. 10 (October 2021): 3097–113. http://dx.doi.org/10.1109/jsac.2021.3088693.

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Zhang, Liang, and Nirwan Ansari. "Optimizing the Operation Cost for UAV-Aided Mobile Edge Computing." IEEE Transactions on Vehicular Technology 70, no. 6 (June 2021): 6085–93. http://dx.doi.org/10.1109/tvt.2021.3076980.

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Ho-Van, Khuong, and Thiem Do-Dac. "Performance Analysis of Energy Harvesting UAV Selection." Wireless Communications and Mobile Computing 2021 (March 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/5545910.

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In an UAV- (unmanned aerial vehicle-) aided relaying system, the transmitted signal is exposed to free space in two transmission hops which may be overheard by eavesdroppers. Accordingly, physical layer security should be exploited to improve information security. This paper analyzes both (security and reliability) performance aspects of such a system where only one UAV among multiple UAVs, all capable of harvesting energy from radio frequency signals, is adopted. Towards this end, the tight approximated and exact closed-form expressions of the outage probability at the legitimate destination and the intercept probability at the eavesdropper are first derived. Then, Monte-Carlo simulations are conducted to verify the derived expressions. Based on these expressions, the protected zone of the selected UAV is also proposed through an exhaustive search. Finally, various results are provided to illustrate the impact of key operation parameters on the system performance and the efficacy of the UAV selection.

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Han, Rui, Yongqing Wen, Lin Bai, Jianwei Liu, and Jinho Choi. "Rate Splitting on Mobile Edge Computing for UAV-Aided IoT Systems." IEEE Transactions on Cognitive Communications and Networking 6, no. 4 (December 2020): 1193–203. http://dx.doi.org/10.1109/tccn.2020.3012680.

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He, Chao, and Jia Xiao. "Joint Optimization in Intelligent Reflecting Surface-Aided UAV Communication for Multiaccess Edge Computing." Wireless Communications and Mobile Computing 2022 (March 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/5415562.

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Intelligent reflecting surface (IRS) is a key enabling technology for b5G and 6G networks, which can provide a reconfigurable electromagnetic environment while reducing energy consumption. In this article, the communication link between user equipment (UE) and the base station (BS) is severely blocked, so we deployed IRS on the Unmanned Aerial Vehicle (UAV) to assist UE for offloading the computing task to the multiaccess edge computing (MEC) server on the base station, which provides mobile users with low-latency edge computing services. By jointly optimizing active beamforming of UE transmitter, passive beamforming of the IRS, UAV hovering position, and computing task scheduling, the response time of user tasks is minimized. In order to solve this complex nonconvex problem, we propose an alternating optimization (AO) algorithm combined with the genetic algorithm to decouple the problem, alternate optimization, until the convergence condition is met, to find the approximate optimal solution of the problem. Numerical results show that with the assistance of IRS, MIMO channels can significantly improve the performance of edge computing and meet the needs of users for high speed and low latency.

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Ma, Ruofei, Ruisong Wang, Gongliang Liu, Weixiao Meng, and Xiqing Liu. "UAV-Aided Cooperative Data Collection Scheme for Ocean Monitoring Networks." IEEE Internet of Things Journal 8, no. 17 (September 1, 2021): 13222–36. http://dx.doi.org/10.1109/jiot.2021.3065740.

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Guo, Xufeng, Yuanbin Chen, and Ying Wang. "Learning-Based Robust and Secure Transmission for Reconfigurable Intelligent Surface Aided Millimeter Wave UAV Communications." IEEE Wireless Communications Letters 10, no. 8 (August 2021): 1795–99. http://dx.doi.org/10.1109/lwc.2021.3081464.

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Verdone, Roberto, and Silvia Mignardi. "Joint Aerial-Terrestrial Resource Management in UAV-Aided Mobile Radio Networks." IEEE Network 32, no. 5 (September 2018): 70–75. http://dx.doi.org/10.1109/mnet.2018.1800036.

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Wang, Zuyan, Jun Tao, Yang Gao, Yifan Xu, Weice Sun, Yu Gao, and Wenqiang Li. "Joint flight scheduling and task allocation for secure data collection in UAV-aided IoTs." Computer Networks 207 (April 2022): 108849. http://dx.doi.org/10.1016/j.comnet.2022.108849.

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