A Survey on Air-Ground Networks of Unmanned Aerial Vehicles

JU Honghao; CHENG Kaijun; DENG Cailian; YAN Xuezhen; YIN Baolin; LONG Yan; FANG Xuming

doi:10.3969/j.issn.0258-2724.20230646

Volume 59 Issue 4

Jul. 2024

Turn off MathJax

Article Contents

Abstract

References

Journal of Southwest Jiaotong University > 2024 > 59(4): 877-889.

JU Honghao, CHENG Kaijun, DENG Cailian, YAN Xuezhen, YIN Baolin, LONG Yan, FANG Xuming. A Survey on Air-Ground Networks of Unmanned Aerial Vehicles[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 877-889. doi: 10.3969/j.issn.0258-2724.20230646

Citation:

JU Honghao, CHENG Kaijun, DENG Cailian, YAN Xuezhen, YIN Baolin, LONG Yan, FANG Xuming. A Survey on Air-Ground Networks of Unmanned Aerial Vehicles[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 877-889. doi: 10.3969/j.issn.0258-2724.20230646

Citation:

PDF( 2059 KB)

A Survey on Air-Ground Networks of Unmanned Aerial Vehicles

doi: 10.3969/j.issn.0258-2724.20230646

Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 611752, China

Received Date: 05 Dec 2023
Rev Recd Date: 09 Mar 2024

Available Online: 25 Apr 2024

Publish Date: 16 Apr 2024

Abstract

Abstract

Unmanned aerial vehicles (UAVs) can be rapidly and cost-effectively deployed. By deploying the base station equipment to the launching UAV platform, the air-ground networks of UAVs can quickly build ground coverage network from the air, so it has broad application prospects in emergency relief, remote area coverage, intelligent transportation, smart city, and other aspects and has received wide attention in recent years. Based on the application scenario of air-ground networks of UAVs, the characteristics of UAVs’maneuver, network, and load were considered. From the four dimensions of coverage performance improvement of air-ground networks, integrated communication-sensing-calculation design of air-ground networks, reconfigurable intelligent surfaces (RIS)-assisted air-ground networks, and robust air-ground networks of UAVs, the research status of air-ground networks of UAVs was reviewed in terms of network scenarios, key technical challenges, and performance optimization control methods. In addition, the future research direction of optimizing the performance of air-ground networks of UAVs was explored.
- UAV,
- network coverage,
- integrated communication-sensing-calculation,
- reconfigurable intelligent surfaces-assisted,
- robust air-ground network of UAVs

FullText(HTML)

References(81)

References

[1]	REINA D G, CAMP T, MUNJAL A, et al. Evolutionary deployment and local search-based movements of 0th responders in disaster scenarios[J]. Future Generation Computer Systems, 2018, 88: 61-78. doi: 10.1016/j.future.2018.05.024
[2]	ZHAO H T, WANG H J, WU W Y, et al. Deployment algorithms for UAV airborne networks toward on-demand coverage[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(9): 2015-2031. doi: 10.1109/JSAC.2018.2864376
[3]	SHAYBOUB M, REDA E, AL-MAHDI H, et al. An efficient framework for sensor data collection by UAV based on clustering, two-fold ant colony optimization and node grouping[EB/OL]. [2023-11-12]. https://doi.org/10.21203/rs.3.rs-3079277/v1.
[4]	BENMAD I, DRIOUCH E, KARDOUCHI M. Trajectory planning for data collection in multi-UAV assisted WSNs[C]//2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring). Helsinki: IEEE, 2022: 1-6
[5]	MENG K T, WU Q Q, MA S D, et al. UAV trajectory and beamforming optimization for integrated periodic sensing and communication[J]. IEEE Wireless Communications Letters, 2022, 11(6): 1211-1215. doi: 10.1109/LWC.2022.3161338
[6]	DENG C L, FANG X M, WANG X B. Beamforming design and trajectory optimization for UAV-empowered adaptable integrated sensing and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 8512-8526. doi: 10.1109/TWC.2023.3264523
[7]	WANG X Y, FEI Z S, ZHANG J A, et al. Constrained utility maximization in dual-functional radar-communication multi-UAV networks[J]. IEEE Transactions on Communications, 2021, 69(4): 2660-2672. doi: 10.1109/TCOMM.2020.3044616
[8]	LIU W C, JIN Z Z, ZHANG X H, et al. AoI-aware UAV-enabled marine MEC networks with integrated sensing, computation, and communication[C]//2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops). Dalian: IEEE, 2023: 1-6.
[9]	XU Y, ZHANG T K, LIU Y W, et al. Computation capacity enhancement by joint UAV and RIS design in IoT[J]. IEEE Internet of Things Journal, 2022, 9(20): 20590-20603. doi: 10.1109/JIOT.2022.3178983
[10]	XU Y, ZHANG T K, ZOU Y X, et al. Reconfigurable intelligence surface aided UAV-MEC systems with NOMA[J]. IEEE Communications Letters, 2022, 26(9): 2121-2125. doi: 10.1109/LCOMM.2022.3183285
[11]	MEI H B, YANG K, SHEN J, et al. Joint trajectory-task-cache optimization with phase-shift design of RIS-assisted UAV for MEC[J]. IEEE Wireless Communications Letters, 2021, 10(7): 1586-1590. doi: 10.1109/LWC.2021.3074990
[12]	QIN X T, SONG Z Y, HOU T W, et al. Joint optimization of resource allocation, phase shift, and UAV trajectory for energy-efficient RIS-assisted UAV-enabled MEC systems[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(4): 1778-1792. doi: 10.1109/TGCN.2023.3287604
[13]	向庭立,王红军,杨刚,等. 分布式无人机网络覆盖优化算法[J]. 空军工程大学学报(自然科学版),2019,20(4): 59-65. XIANG Tingli, WANG Hongjun, YANG Gang, et al. Research on distributed UAV network coverage optimization algorithm[J]. Journal of Air Force Engineering University (Natural Science Edition), 2019, 20(4): 59-65.
[14]	AKRAM T, AWAIS M, NAQVI R, et al. Multicriteria UAV base stations placement for disaster management[J]. IEEE Systems Journal, 2020, 14(3): 3475-3482. doi: 10.1109/JSYST.2020.2970157
[15]	KIMURA T, OGURA M. Distributed collaborative 3D-deployment of UAV base stations for on-demand coverage[C]//IEEE INFOCOM 2020-IEEE Conference on Computer Communications. Toronto: IEEE, 2020: 1748-1757.
[16]	SHAKOOR S, KALEEM Z, DO D T, et al. Joint optimization of UAV 3-D placement and path-loss factor for energy-efficient maximal coverage[J]. IEEE Internet of Things Journal, 2021, 8(12): 9776-9786. doi: 10.1109/JIOT.2020.3019065
[17]	VO V N, NGUYEN L M D, TRAN H, et al. Outage probability minimization in secure NOMA cognitive radio systems with UAV relay: a machine learning approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2023, 9(2): 435-451. doi: 10.1109/TCCN.2022.3226184
[18]	ANICHO O, CHARLESWORTH P B, BAICHER G S, et al. Comparative study for coordinating multiple unmanned HAPS for communications area coverage[C]//2019 International Conference on Unmanned Aircraft Systems (ICUAS). Atlanta: IEEE, 2019: 467-474.
[19]	YAN X Z, FANG X M, DENG C L, et al. Joint optimization of resource allocation and trajectory control for mobile group users in fixed-wing UAV-enabled wireless network[J]. IEEE Transactions on Wireless Communications, 2024, 23(2): 1608-1621. doi: 10.1109/TWC.2023.3290748
[20]	LIU X, LIU Y W, CHEN Y, et al. Trajectory design and power control for multi-UAV assisted wireless networks: a machine learning approach[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 7957-7969. doi: 10.1109/TVT.2019.2920284
[21]	MOU Z Y, ZHANG Y, GAO F F, et al. Deep reinforcement learning based three-dimensional area coverage with UAV swarm[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(10): 3160-3176. doi: 10.1109/JSAC.2021.3088718
[22]	JIA Y H, ZHOU S B, ZENG Q, et al. The UAV path coverage algorithm based on the greedy strategy and ant colony optimization[J]. Electronics, 2022, 11(17): 2667.1-2667.15.
[23]	HU W J, YU Y, LIU S M, et al. Multi-UAV coverage path planning: a distributed online cooperation method[J]. IEEE Transactions on Vehicular Technology, 2023, 72(9): 11727-11740. doi: 10.1109/TVT.2023.3266817
[24]	王巍,梁雅静,刘阳,等. 城市灾区无人机网络自适应覆盖优化算法[J]. 计算机工程与应用,2022,58(14): 258-268. doi: 10.3778/j.issn.1002-8331.2010-0211 WANG Wei, LIANG Yajing, LIU Yang, et al. Adaptive coverage optimization algorithm for drone network in urban disaster areas[J]. Computer Engineering and Applications, 2022, 58(14): 258-268. doi: 10.3778/j.issn.1002-8331.2010-0211
[25]	WANG L, WU X W, WANG Y H, et al. On UAV serving node deployment for temporary coverage in forest environment: a hierarchical deep reinforcement learning approach[J]. Chinese Journal of Electronics, 2023, 32(4): 760-772. doi: 10.23919/cje.2021.00.326
[26]	ABEYWICKRAMA H V, HE Y, DUTKIEWICZ E, et al. A reinforcement learning approach for fair user coverage using UAV mounted base stations under energy constraints[J]. IEEE Open Journal of Vehicular Technology, 2020, 1: 67-81. doi: 10.1109/OJVT.2020.2971594
[27]	PAN W, LYU N, MIAO J C, et al. Outage probability optimization of UAV relay system based on elliptical trajectory[J]. Wireless Networks, 2023, 29: 3285-3294. doi: 10.1007/s11276-023-03387-5
[28]	JIANG X, YIN Z D, WU Z L, et al. Outage probability optimization for UAV-enabled wireless relay networks in fading channels[J]. Physical Communication, 2019, 33: 35-45. doi: 10.1016/j.phycom.2018.12.013
[29]	HUA M, WANG Y, ZHANG Z M, et al. Outage probability minimization for low-altitude UAV-enabled full-duplex mobile relaying systems[J]. China Communications, 2018, 15(5): 9-24. doi: 10.1109/CC.2018.8387983
[30]	LIU Z X, TIAN Q L, XIE Y A, et al. Outage probability minimization for vehicular networks via joint clustering, UAV trajectory optimization and power allocation[J]. Ad Hoc Networks, 2023, 140(2): 103060.1-103060.11.
[31]	LIU X, DURRANI T S. Joint multi-UAV deployments for air—ground integrated networks[J]. IEEE Aerospace and Electronic Systems Magazine, 2022, 37(12): 4-12. doi: 10.1109/MAES.2022.3220725
[32]	WU Q Q, ZHANG R. Common throughput maximization in UAV-enabled OFDMA systems with delay consideration[J]. IEEE Transactions on Communications, 2018, 66(12): 6614-6627. doi: 10.1109/TCOMM.2018.2865922
[33]	DU W B, YING W, YANG P, et al. Network-based heterogeneous particle swarm optimization and its application in UAV communication coverage[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2020, 4(3): 312-323. doi: 10.1109/TETCI.2019.2899604
[34]	RIFKIN J. The zero marginal cost society: The internet of things, the collaborative commons, and the eclipse of capitalism[J]. The Journal of Sustainable Mobility, 2015, 2: 67-70. doi: 10.9774/GLEAF.2350.2015.de.00007
[35]	SERIES M. Framework and overall objectives of the future development of IMT for 2030 and beyond. Source: ITU-R internal document [EB/OL]. [2023-11-12]. https://www.itu.int/md/meetingdoc.asp?lang=en&parent=R19SG05-C-0131.
[36]	ZHANG J A, LIU F, MASOUROS C, et al. An overview of signal processing techniques for joint communication and radar sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1295-1315. doi: 10.1109/JSTSP.2021.3113120
[37]	LIU F, MASOUROS C, PETROPULU A P, et al. Joint radar and communication design: applications, state-of-the-art, and the road ahead[J]. IEEE Transactions on Communications, 2020, 68(6): 3834-3862. doi: 10.1109/TCOMM.2020.2973976
[38]	ALNOMAN A, SHARMA S K, EJAZ W, et al. Emerging edge computing technologies for distributed IoT systems[J]. IEEE Network, 2019, 33(6): 140-147. doi: 10.1109/MNET.2019.1800543
[39]	TRAN T X, HAJISAMI A, PANDEY P, et al. Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges[J]. IEEE Communications Magazine, 2017, 55(4): 54-61. doi: 10.1109/MCOM.2017.1600863
[40]	WANG Y G, WANG H, WEI X L. Energy-efficient UAV deployment and task scheduling in multi-UAV edge computing[C]//2020 International Conference on Wireless Communications and Signal Processing (WCSP). Nanjing: IEEE, 2020: 1147-1152.
[41]	XU Y J, ZHOU H, DENG Y S. Task-oriented semantics-aware communication for wireless UAV control and command transmission[J]. IEEE Communications Letters, 2023, 27(8): 2232-2236. doi: 10.1109/LCOMM.2023.3290109
[42]	ZHOU J S, TIAN D X, SHENG Z G, et al. Joint mobility, communication and computation optimization for UAVs in air-ground cooperative networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(3): 2493-2507. doi: 10.1109/TVT.2021.3059964
[43]	LIU Z W, CAO Y, GAO P, et al. Multi-UAV network assisted intelligent edge computing: challenges and opportunities[J]. China Communications, 2022, 19(3): 258-278. doi: 10.23919/JCC.2022.03.019
[44]	WANG Y T, GUO H Z, LIU J J. Cooperative task offloading in UAV swarm-based edge computing[C]//2021 IEEE Global Communications Conference (GLOBECOM). Madrid: IEEE, 2021: 1-6.
[45]	LYU Z H, ZHU G X, XU J. Joint maneuver and beamforming design for UAV-enabled integrated sensing and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(4): 2424-2440. doi: 10.1109/TWC.2022.3211533
[46]	JING X Y, LIU F, MASOUROS C, et al. ISAC from the sky: UAV trajectory design for joint communication and target localization[EB/OL]. [2023-11-12]. https://arxiv.org/abs/2207.02904.
[47]	LIU Y, ZHOU J S, TIAN D X, et al. Joint communication and computation resource scheduling of a UAV-assisted mobile edge computing system for platooning vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 8435-8450. doi: 10.1109/TITS.2021.3082539
[48]	CHENG K J, FANG X M, WANG X B. Energy efficient edge computing and data compression collaboration scheme for UAV-assisted network[J]. IEEE Transactions on Vehicular Technology, 2023, 72(12): 16395-16408. doi: 10.1109/TVT.2023.3289962
[49]	XIONG J Y, GUO H Z, LIU J J. Task offloading in UAV-aided edge computing: bit allocation and trajectory optimization[J]. IEEE Communications Letters, 2019, 23(3): 538-541. doi: 10.1109/LCOMM.2019.2891662
[50]	GUO F X, ZHANG H L, JI H, et al. Joint trajectory and computation offloading optimization for UAV-assisted MEC with NOMA[C]//IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Paris: IEEE, 2019: 1-6.
[51]	XIA J M, WANG P, LI B, et al. Intelligent task offloading and collaborative computation in multi-UAV-enabled mobile edge computing[J]. China Communications, 2022, 19(4): 244-256. doi: 10.23919/JCC.2022.04.018
[52]	QIN Y H, ZHANG Z S, LI X L, et al. Deep reinforcement learning based resource allocation and trajectory planning in integrated sensing and communications UAV network[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 8158-8169. doi: 10.1109/TWC.2023.3260304
[53]	OUYANG W J, MU J S, ZHANG R H, et al. Intelligent fusion of integrated sensing and communication signal on the UAV platform[C]//2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops). Foshan: IEEE, 2022: 483-487.
[54]	WANG M, CHEN P, CAO Z X, et al. Reinforcement learning-based UAVs resource allocation for integrated sensing and communication (ISAC) system[J]. Electronics, 2022, 11(3): 441.1-441.15.
[55]	ZHOU G, PAN C H, REN H, et al. Robust beamforming design for intelligent reflecting surface aided MISO communication systems[J]. IEEE Wireless Communications Letters, 2020, 9(10): 1658-1662. doi: 10.1109/LWC.2020.3000490
[56]	WU Q Q, ZHANG R. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58(1): 106-112. doi: 10.1109/MCOM.001.1900107
[57]	WU Q Q, ZHANG S W, ZHENG B X, et al. Intelligent reflecting surface-aided wireless communications: a tutorial[J]. IEEE Transactions on Communications, 2021, 69(5): 3313-3351. doi: 10.1109/TCOMM.2021.3051897
[58]	SU Y H, PANG X W, LU W D, et al. Joint location and beamforming optimization for STAR-RIS aided NOMA-UAV networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(8): 11023-11028. doi: 10.1109/TVT.2023.3261324
[59]	ZHANG Q, ZHAO Y, LI H, et al. Joint optimization of STAR-RIS assisted UAV communication systems[J]. IEEE Wireless Communications Letters,2022,11(11):2390-2394.
[60]	NGUYEN K K, MASARACCHIA A, SHARMA V, et al. RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2022, 16(5): 1086-1096. doi: 10.1109/JSTSP.2022.3172587
[61]	FAN X K, LIU M, CHEN Y L, et al. RIS-assisted UAV for fresh data collection in 3D urban environments: a deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2023, 72(1): 632-647. doi: 10.1109/TVT.2022.3203008
[62]	YANG B W, YU Y, LI J Q, et al. An AoI-guaranteed sensor data collection strategy for RIS-assisted UAV communication system[C]//2023 IEEE/CIC International Conference on Communications in China (ICCC). Dalian: IEEE, 2023: 1-6.
[63]	REN H, ZHANG Z K, PENG Z J, et al. Energy minimization in RIS-assisted UAV-enabled wireless power transfer systems[J]. IEEE Internet of Things Journal,2023,10(7):5794-5809.
[64]	LIU Q L, HAN J, LIU Q. Joint task offloading and resource allocation for RIS-assisted UAV for mobile edge computing networks[C]//2023 IEEE/CIC International Conference on Communications in China (ICCC). Dalian: IEEE, 2023: 1-6.
[65]	ZHAI Z Y, DAI X H, DUO B, et al. Energy-efficient UAV-mounted RIS assisted mobile edge computing[J]. IEEE Wireless Communications Letters, 2022, 11(12): 2507-2511. doi: 10.1109/LWC.2022.3206587
[66]	DUO B, HE M L, WU Q Q, et al. Joint dual-UAV trajectory and RIS design for ARIS-assisted aerial computing in IoT[J]. IEEE Internet of Things Journal, 2023, 10(22): 19584-19594. doi: 10.1109/JIOT.2023.3288213
[67]	LIU Z H, WANG X K, SHEN L C, et al. Mission-oriented miniature fixed-wing UAV swarms: a multilayered and distributed architecture[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(3): 1588-1602.
[68]	DING Y L, XIN B, CHEN J. Curvature-constrained path elongation with expected length for Dubins vehicle[J]. Automatica, 2019, 108: 108495.1-108495.17.
[69]	XIAO Y, YE Y, HUANG S C, et al. Fully decentralized federated learning-based on-board mission for UAV swarm system[J]. IEEE Communications Letters, 2021, 25(10): 3296-3300. doi: 10.1109/LCOMM.2021.3095362
[70]	HOU X W, WANG J J, JIANG C X, et al. UAV-enabled covert federated learning[J]. IEEE Transactions on Wireless Communications, 2023, 22(10): 6793-6809. doi: 10.1109/TWC.2023.3245621
[71]	TANG J H, NIE J T, ZHANG Y, et al. Multi-UAV-assisted federated learning for energy-aware distributed edge training[J]. IEEE Transactions on Network and Service Management, 2024, 21(1): 280-294. doi: 10.1109/TNSM.2023.3298220
[72]	DONG F Y, LI L P, LU Z M, et al. Energy-efficiency for fixed-wing UAV-enabled data collection and forwarding[C]//2019 IEEE International Conference on Communications Workshops (ICC Workshops). Shanghai: IEEE, 2019: 1-6.
[73]	YOON J, LEE A H, LEE H. Rendezvous: opportunistic data delivery to mobile users by UAVs through target trajectory prediction[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 2230-2245. doi: 10.1109/TVT.2019.2962391
[74]	LIU J J, KATO N, MA J F, et al. Throughput and delay tradeoffs for mobile Ad Hoc networks with reference point group mobility[J]. IEEE Transactions on Wireless Communications, 2015, 14(3): 1266-1279. doi: 10.1109/TWC.2014.2365553
[75]	WANG Y, LI Z D, CHEN Y B, et al. Joint resource allocation and UAV trajectory optimization for space–air–ground Internet of remote things networks[J]. IEEE Systems Journal, 2021, 15(4): 4745-4755. doi: 10.1109/JSYST.2020.3019463
[76]	曾晓婉,王海军,马东堂,等. 面向最大化服务用户数的无人机基站3D部署方法[J]. 电讯技术,2023,63(11): 1732-1741. ZENG Xiaowan, WANG Haijun, MA Dongtang, et al. A UAV base station 3D deployment method for maximum users served[J]. Telecommunication Engineering, 2023, 63(11): 1732-1741.
[77]	LI R D, WEI Z Q, YANG L, et al. Resource allocation for secure multi-UAV communication systems with multi-eavesdropper[J]. IEEE Transactions on Communications, 2020, 68(7): 4490-4506. doi: 10.1109/TCOMM.2020.2983040
[78]	ZHANG Y, MOU Z Y, GAO F F, et al. UAV-enabled secure communications by multi-agent deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 11599-11611. doi: 10.1109/TVT.2020.3014788
[79]	YANG H L, ZHAO J, XIONG Z H, et al. Privacy-preserving federated learning for UAV-enabled networks: learning-based joint scheduling and resource management[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(10): 3144-3159. doi: 10.1109/JSAC.2021.3088655
[80]	LIN Y, WANG M Y, ZHOU X L, et al. Dynamic spectrum interaction of UAV flight formation communication with priority: a deep reinforcement learning approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(3): 892-903 doi: 10.1109/TCCN.2020.2973376
[81]	OUAHOUAH S, BAGAA M, PRADOS-GARZON J, et al. Deep-reinforcement-learning-based collision avoidance in UAV environment[J]. IEEE Internet of Things Journal, 2022, 9(6): 4015-4030. doi: 10.1109/JIOT.2021.3118949

Relative Articles

[1]	WU Xinwei, HU Minghua, MAO Jizhi, WANG Yang. Collaborative Target Azimuth Perception Algorithm of Unmanned Aerial Vehicles Based on Spatial Spectrum Estimation[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 898-906, 932. doi: 10.3969/j.issn.0258-2724.20230438
[2]	ZHOU Jingxuan, BAO Weidong, WANG Ji, ZHANG Dayu. Multi-Task Federated Learning for Unmanned Aerial Vehicle Swarms Based on Encoder-Decoder Architecture[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 933-941. doi: 10.3969/j.issn.0258-2724.20230539
[3]	YE Qibin, XIAO Hongyu, TIAN Chen, LIU Ming, FU Yunlin, HU Su. Self-Interference Cancellation Technology of Integrated Sensing and Communications System for Unmanned Aerial Vehicles[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 925-932. doi: 10.3969/j.issn.0258-2724.20230599
[4]	QIN Qiancong, WU Guanlin, GAO Yuan, WANG Shuangshuang, LI Peng. Distributed Storage Methods for Unmanned Aerial Vehicle Clusters in Battlefield[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 942-958. doi: 10.3969/j.issn.0258-2724.20230521
[5]	ZHANG Xianyu, CHEN Yong, ZHANG Yu, YANG Hua. Joint Optimization of Resource Allocation and Deployment Location in Unmanned Aerial Vehicle-Assisted Communication[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 917-924. doi: 10.3969/j.issn.0258-2724.20230400
[6]	CUI Yaping, YING Zhaopeng, HE Peng, ZHENG Yufeng, WU Dapeng, WANG Ruyan, CHEN Luo. Ultra-Reliable Low-Latency Communication Multi-Unmanned Aerial Vehicle Network Assisted by Intelligent Reflecting Surface in Air[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 907-916. doi: 10.3969/j.issn.0258-2724.20230288
[7]	GUO Yang, GAO Yuan, CHENG Shaochi, WANG Xiaonan. Optimization Control Strategy for Low-Altitude and Single-Layer Unmanned Aerial Vehicle Network Coverage[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 890-897. doi: 10.3969/j.issn.0258-2724.20230535
[8]	YU Wenjuan, LI Decai, ZHANG Zhili. Magnetic Fluid Sealing Structure of Integrated Pole Shoe[J]. Journal of Southwest Jiaotong University, 2023, 58(4): 940-946. doi: 10.3969/j.issn.0258-2724.20210936
[9]	WANG Jialiang, DONG Kai, GU Zhaojun, CHEN Hui, HAN Qiang. Recognition Algorithm of Safe Obstacle Avoidance Domain for UAVs Based on Maximization Idea[J]. Journal of Southwest Jiaotong University, 2023, 58(6): 1267-1276. doi: 10.3969/j.issn.0258-2724.20220262
[10]	WANG Jian, QIN Chunxia, YANG Ke, REN Ping, ZHENG Jie, ZHAO Yuanpeng, CHEN Guifeng. Design of Airborne Video Dehazing System for UCAV Based on HSV Transmission Weighted Correction[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 160-167, 205. doi: 10.3969/j.issn.0258-2724.20190985
[11]	LI Manman, LU Jian, ZHANG He. Vehicle Routing Problem with Pick-up and Delivery under Dynamic Travel Times[J]. Journal of Southwest Jiaotong University, 2019, 54(5): 1104-1112. doi: 10.3969/j.issn.0258-2724.20170488
[12]	ZHU Xinghui, WU Weiwei, QI Yanlong. Robust Optimization Model for Integrated Aircraft Scheduling Based on Delay Propagation[J]. Journal of Southwest Jiaotong University, 2015, 28(2): 375-381. doi: 10.3969/j.issn.0258-2724.2015.02.026
[13]	SHU Li, LIN Feng, LIU Zhonghao, YOU Jian, ZHOU Jiliu. POI Classfication Based Sweep Coverage Scheme in Wireless Sensor Networks[J]. Journal of Southwest Jiaotong University, 2014, 27(1): 165-172. doi: 10.3969/j.issn.0258-2724.2014.01.026
[14]	WANG Yu, ZHU Jinfu, GE Wei. Airline Fleet Robust Optimization Approach under Stochastic Demand with Route Network Effects[J]. Journal of Southwest Jiaotong University, 2014, 27(4): 727-733. doi: 10.3969/j.issn.0258-2724.2014.04.026
[15]	WU Xiaohuan, ZHU Jinfu, WU Weiwei, GAO Qiang. Interval Robust Optimization of Airline Network Designing[J]. Journal of Southwest Jiaotong University, 2013, 26(3): 559-564. doi: 10.3969/j.issn.0258-2724.2013.03.026
[16]	HE Dong, ZHU Jianmei. Building Modes of Integrative Passenger Rail Transit System[J]. Journal of Southwest Jiaotong University, 2012, 25(2): 279-284. doi: 10.3969/j.issn.0258-2724.2012.02.018
[17]	HE Jing, LI Yongshu, LI Xin, TANG Min. Registration Method for Unmanned Aerial Vehicle Images Based on Point Feature and Edge Feature[J]. Journal of Southwest Jiaotong University, 2012, 25(6): 955-961. doi: 10.3969/j.issn.0258-2724.2012.06.008
[18]	TANG Min, LI Yong-Shu, HE Jing. Relative Orientation Method of UAV Images without Ground Control Points[J]. Journal of Southwest Jiaotong University, 2011, 24(5): 808-813. doi: 10.3969/j.issn.0258-2724.2011.05.016
[19]	SHI Ji-Zhong, XU Xiao-Hui, ZHANG Ji-Ye. Robust Stability of Impulsive Cohen-Grossberg Neural Networks with Reaction-Diffusion Terms[J]. Journal of Southwest Jiaotong University, 2010, 23(4): 596-602. doi: 10. 3969/ j. issn. 0258-2724.
[20]	LU Heng, LI Yong-Shu, LI He-Chao, HE Jing, REN Zhi-Ming. Digital Processing of Unmanned Aerial Vehicle Image and Its Application in Reconstruction of Wenchuan Earthquake-Hit Areas[J]. Journal of Southwest Jiaotong University, 2010, 23(4): 533-538. doi: 10. 3969/ j. issn. 0258-2724.

Supplements(0)

Cited By

Cited by

Periodical cited type(3)

1.	李书缘，张敏，曾凡喾，高月红. 无人机空地网络算力资源预测与分配联合算法研究. 移动通信. 2024(09): 116-123 .
2.	吴力华，胡爽，曹锐. 中心级多目标测量数据融合的设计与实现. 现代电子技术. 2024(23): 131-137 .
3.	黄嘉伟，黎海涛，李哲超. 无人机网络计算卸载和轨迹控制联合优化策略. 中国电子科学研究院学报. 2024(07): 615-621 .

Other cited types(0)

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(4) / Tables(1)

Get Citation

PDF

XML

Article views(1274) PDF downloads(330)