Gotowa bibliografia na temat „UAV-aided Wireless Networks”
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Artykuły w czasopismach na temat "UAV-aided Wireless Networks"
Teng, Sihao. "UAV Assisted Wireless Network". Journal of Physics: Conference Series 2078, nr 1 (1.11.2021): 012022. http://dx.doi.org/10.1088/1742-6596/2078/1/012022.
Pełny tekst źródłaArafat, Muhammad Yeasir, Md Arafat Habib i Sangman Moh. "Routing Protocols for UAV-Aided Wireless Sensor Networks". Applied Sciences 10, nr 12 (12.06.2020): 4077. http://dx.doi.org/10.3390/app10124077.
Pełny tekst źródłaIakovlev, Roman, i Anton Saveliev. "Approach to implementation of local navigation of mobile robotic systems in agriculture with the aid of radio modules". Telfor Journal 12, nr 2 (2020): 92–97. http://dx.doi.org/10.5937/telfor2002092i.
Pełny tekst źródłaHua, Meng, Yi Wang, Zhengming Zhang, Chunguo Li, Yongming Huang i Luxi Yang. "Power-Efficient Communication in UAV-Aided Wireless Sensor Networks". IEEE Communications Letters 22, nr 6 (czerwiec 2018): 1264–67. http://dx.doi.org/10.1109/lcomm.2018.2822700.
Pełny tekst źródłaHua, Meng, Yi Wang, Zhengming Zhang, Chunguo Li, Yongming Huang i Luxi Yang. "Energy-efficient optimisation for UAV-aided wireless sensor networks". IET Communications 13, nr 8 (14.05.2019): 972–80. http://dx.doi.org/10.1049/iet-com.2018.5441.
Pełny tekst źródłaLiu, Bin, i Hongbo Zhu. "Energy-Effective Data Gathering for UAV-Aided Wireless Sensor Networks". Sensors 19, nr 11 (31.05.2019): 2506. http://dx.doi.org/10.3390/s19112506.
Pełny tekst źródłaBerrahal, Sarra, Jong-Hoon Kim, Slim Rekhis, Noureddine Boudriga, Deon Wilkins i Jaime Acevedo. "Border surveillance monitoring using Quadcopter UAV-Aided Wireless Sensor Networks". Journal of Communications Software and Systems 12, nr 1 (22.03.2016): 67. http://dx.doi.org/10.24138/jcomss.v12i1.92.
Pełny tekst źródłaShi, Baihua, Yang Wang, Danqi Li, Wenlong Cai, Jinyong Lin, Shuo Zhang, Weiping Shi, Shihao Yan i Feng Shu. "STAR-RIS-UAV-Aided Coordinated Multipoint Cellular System for Multi-User Networks". Drones 7, nr 6 (17.06.2023): 403. http://dx.doi.org/10.3390/drones7060403.
Pełny tekst źródłaCao, Dongju, Wendong Yang i Gangyi Xu. "Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks". Applied Sciences 9, nr 24 (15.12.2019): 5524. http://dx.doi.org/10.3390/app9245524.
Pełny tekst źródłaCastellanos, German, Margot Deruyck, Luc Martens i Wout Joseph. "Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks". Sensors 19, nr 15 (30.07.2019): 3342. http://dx.doi.org/10.3390/s19153342.
Pełny tekst źródłaRozprawy doktorskie na temat "UAV-aided Wireless Networks"
Bayerlein, Harald. "Machine Learning Methods for UAV-aided Wireless Networks". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS154.
Pełny tekst źródłaAutonomous unmanned aerial vehicles (UAVs), spurred by rapid innovation in drone hardware and regulatory frameworks during the last decade, are envisioned for a multitude of applications in service of the society of the future. From the perspective of next-generation wireless networks, UAVs are not only anticipated in the role of passive cellular-connected users, but also as active enablers of connectivity as part of UAV-aided networks. The defining advantage of UAVs in all potential application scenarios is their mobility. To take full advantage of their capabilities, flexible and efficient path planning methods are necessary. This thesis focuses on exploring machine learning (ML), specifically reinforcement learning (RL), as a promising class of solutions to UAV mobility management challenges. Deep RL is one of the few frameworks that allows us to tackle the complex task of UAV control and deployment in communication scenarios directly, given that these are generally NP-hard optimization problems and badly affected by non-convexity. Furthermore, deep RL offers the possibility to balance multiple objectives of UAV-aided networks in a straightforward way, it is very flexible in terms of the availability of prior or model information, while deep RL inference is computationally efficient. This thesis also explores the challenges of severely limited flying time, cooperation between multiple UAVs, and reducing the training data demand of DRL methods. The thesis also explores the connection between drone-assisted networks and robotics, two generally disjoint research communities
Części książek na temat "UAV-aided Wireless Networks"
Shi, Weisen, Junling Li i Ning Zhang. "Resource Allocation in UAV-Aided Wireless Networks". W Encyclopedia of Wireless Networks, 1222–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_345.
Pełny tekst źródłaShi, Weisen, Junling Li i Ning Zhang. "Resource Allocation in UAV-Aided Wireless Networks". W Encyclopedia of Wireless Networks, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_345-1.
Pełny tekst źródła"UAV-Aided Wireless Network". W Encyclopedia of Wireless Networks, 1423. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_300676.
Pełny tekst źródłaStreszczenia konferencji na temat "UAV-aided Wireless Networks"
Esrafilian, Omid, Rajeev Gangula i David Gesbert. "Autonomous UAV-aided Mesh Wireless Networks". W IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2020. http://dx.doi.org/10.1109/infocomwkshps50562.2020.9162753.
Pełny tekst źródłaMa, Xiaoyan, Tianyi Liu, Rahim Kacimi, Riadh Dhaou i Song Liu. "Duration-aware Data Collection in UAV-aided Mobile Sensor Networks". W 2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021. http://dx.doi.org/10.1109/iwcmc51323.2021.9498971.
Pełny tekst źródłaYang, Longan, Zhiyuan Su, Haibing Yang, Zhixiong Na i Feng Yan. "An Efficient Charging Algorithm for UAV-aided Wireless Sensor Networks". W 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc51575.2020.9345142.
Pełny tekst źródłaXue, Sheng, Suzhi Bi i Xiaohui Lin. "Energy Minimization in UAV-Aided Wireless Sensor Networks with OFDMA". W 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2019. http://dx.doi.org/10.1109/wcsp.2019.8927916.
Pełny tekst źródłaMarini, Riccardo, Sangwoo Park, Osvaldo Simeone i Chiara Buratti. "Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks". W ICC 2023 - IEEE International Conference on Communications. IEEE, 2023. http://dx.doi.org/10.1109/icc45041.2023.10279524.
Pełny tekst źródłaLakiotakis, Emmanouil, Nikolaos Pappas i Xenofontas Dimitropoulos. "Modeling the Age of Information in UAV-aided Wireless Networks". W 2022 IEEE Conference on Standards for Communications and Networking (CSCN). IEEE, 2022. http://dx.doi.org/10.1109/cscn57023.2022.10051021.
Pełny tekst źródłaPang, Xiaowei, Zan Li, Xiaoming Chen, Yang Cao, Nan Zhao, Yunfei Chen i Zhiguo Ding. "UAV-Aided NOMA Networks with Optimization of Trajectory and Precoding". W 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018. http://dx.doi.org/10.1109/wcsp.2018.8555640.
Pełny tekst źródłaSharma, Vatsala, Prajwalita Saikia, Sandeep Kumar Singh, Keshav Singh, Wan-Jen Huang i Sudip Biswas. "FEEL-enhanced Edge Computing in Energy Constrained UAV-aided IoT Networks". W 2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023. http://dx.doi.org/10.1109/wcnc55385.2023.10118939.
Pełny tekst źródłaMao, Shenshen, Feng Yan, Jiahui Chen, Fei Shen, Weiwei Xia, Jin Hu i Lianfeng Shen. "An Energy Efficient Charging Scheme for UAV-aided Wireless Sensor Networks". W 2019 IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, 2019. http://dx.doi.org/10.1109/iccc47050.2019.9064340.
Pełny tekst źródłaChen, Jiahui, Feng Yan, Shenshen Mao, Fei Shen, Weiwei Xia, Yi Wu i Lianfeng Shen. "Efficient Data Collection in Large-Scale UAV-aided Wireless Sensor Networks". W 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2019. http://dx.doi.org/10.1109/wcsp.2019.8927929.
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