Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search

CHEN Jiamei; LI Shiang; LI Yufeng; WANG Yupeng; BIE Yuxia

doi:10.11999/JEIT220404

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1697-1705

CHEN Jiamei, LI Shiang, LI Yufeng, WANG Yupeng, BIE Yuxia. Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404

Citation:

CHEN Jiamei, LI Shiang, LI Yufeng, WANG Yupeng, BIE Yuxia. Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404

Citation:

CHEN Jiamei, LI Shiang, LI Yufeng, WANG Yupeng, BIE Yuxia. Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404

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Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search

doi: 10.11999/JEIT220404

College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110136, China

Funds: The National Natural Science Foundation of China (61901284), The Natural Science Foundation of Liaoning Province (2019-ZD-0220), The Aeronautical Science Foundation of China (201926054001)

Received Date: 2022-04-06
Rev Recd Date: 2022-05-27

Available Online: 2022-05-30

Publish Date: 2023-05-10

Abstract

Abstract

In the case of large gathering of users such as sports venues or sudden disasters, the ground base stations often face the problem of overloading or even paralysing. In this case, the multi-Unmanned Aerial Vehicle (UAV) auxiliary network system can provide the signal compensation for ground base stations and enhance effectively the communication quality in local areas. However, the topology changes induced by the mobility of UAV and the network flows, will lead to frequent intermittent connections or even transmission failures. Therefore, the efficient deployment of UAV base stations, as well as the optimization of network performance, become urgent issues. In this paper, an improved Particle Swarm Optimization (PSO) UAV assisted network deployment optimization algorithm based on the Beetle Antennae Search (BAS), the Intelligent and Efficient Algorithm (IEA), is proposed to improve PSO algorithm by using the individual seeking advantages of BAS algorithm. And for the first time, the double threshold constraint is applied to ensure the communication quality of users, which makes the network performance under the multi-UAV system improved. The simulation results show that, compared with the traditional algorithms, the IEA algorithm proposed in this paper achieves an obvious improvement in terms of the system throughput, the user’s average throughput as well as the spectral efficiency.

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References(17)

References

[1]	DONG Lei, ZHAO Hongyi, CHEN Yan, et al. Introduction on IMT-2020 5G trials in China[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(8): 1849–1866. doi: 10.1109/JSAC.2017.2710678
[2]	陈新颖, 盛敏, 李博, 等. 面向6G的无人机通信综述[J]. 电子与信息学报, 2022, 44(3): 781–789. doi: 10.11999/JEIT210789 CHEN Xinying, SHENG Min, LI Bo, et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics &Information Technology, 2022, 44(3): 781–789. doi: 10.11999/JEIT210789
[3]	YEOM J, HAN Y, CHANG Anjin, et al. Hurricane building damage assessment using post-disaster UAV data[C]. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 9867–9870.
[4]	CHEN Kanghua, WANG Ying, ZHAO Junwei, et al. URLLC-oriented joint power control and resource allocation in UAV-assisted networks[J]. IEEE Internet of Things Journal, 2021, 8(12): 10103–10116. doi: 10.1109/JIOT.2021.3051322
[5]	ZHAO Nan, LU Weidang, SHENG Min, et al. UAV-assisted emergency networks in disasters[J]. IEEE Wireless Communications, 2019, 26(1): 45–51. doi: 10.1109/MWC.2018.1800160
[6]	ZHAN Pengcheng, YU Kai, and SWINDLEHURST A. Wireless relay communications with unmanned aerial vehicles: Performance and optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 2068–2085. doi: 10.1109/TAES.2011.5937283
[7]	ZHANG Shuhang, ZHANG Hongliang, DI Bichen, et al. Joint trajectory and power optimization for UAV relay networks[J]. IEEE Communications Letters, 2018, 22(1): 161–164. doi: 10.1109/LCOMM.2017.2763135
[8]	PEARRE B and BROWN T X. Model-free trajectory optimization for wireless data ferries among multiple sources[C]. IEEE Globecom Workshops, Miami, USA, 2010: 1793–1798.
[9]	PARK J H, CHOI S C, AHN I Y, et al. Multiple UAVs-based surveillance and reconnaissance system utilizing IoT platform[C]. 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand, 2019: 1–3.
[10]	AL-HOURANI A, KANDEEPAN S, and JAMALIPOUR A. Modeling air-to-ground path loss for low altitude platforms in urban environments[C]. 2014 IEEE Global Communications Conference, Austin, USA, 2014: 2898–2904.
[11]	张广驰, 严雨琳, 崔苗, 等. 无人机基站的飞行路线在线优化设计[J]. 电子与信息学报, 2021, 43(12): 3605–3611. doi: 10.11999/JEIT200525 ZHANG Guangchi, YAN Yulin, CUI Miao, et al. Online trajectory optimization for the UAV-mounted base stations[J]. Journal of Electronics &Information Technology, 2021, 43(12): 3605–3611. doi: 10.11999/JEIT200525
[12]	GUO Hongzhi and LIU Jiajia. UAV-enhanced intelligent offloading for internet of things at the edge[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2737–2746. doi: 10.1109/TII.2019.2954944
[13]	ZENG Yong and ZHANG Rui. Energy-efficient UAV communication with trajectory optimization[J]. IEEE Transactions on Wireless Communications, 2017, 16(6): 3747–3760. doi: 10.1109/TWC.2017.2688328
[14]	CUI Jian, SHAKHATREH H, HU Bo, et al. Power-efficient deployment of a UAV for emergency indoor wireless coverage[J]. IEEE Access, 2018, 6: 73200–73209. doi: 10.1109/ACCESS.2018.2882896
[15]	HAN Bing, LI Qingmei, and CHENG Chengqi. Research on UAV indoor path planning algorithm based on global subdivision grids[C]. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021: 8503–8506.
[16]	WANG Lei, HU Bo, and CHEN Shanzhi. Energy efficient placement of a drone base station for minimum required transmit power[J]. IEEE Wireless Communications Letters, 2020, 9(12): 2010–2014. doi: 10.1109/LWC.2018.2808957
[17]	MA Xiaoyong, HU Shuting, ZHOU Danyang, et al. Adaptive deployment of UAV-aided networks based on hybrid deep reinforcement learning[C]. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, Canada, 2020: 1–6.