Joint Trajectory and Resource Allocation Optimization for Air-ground Collaborative Integrated Sensing and Communication Systems

ZHANG Guangchi; GU Zelin; CUI Miao

doi:10.11999/JEIT230716

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 >

ZHANG Guangchi, GU Zelin, CUI Miao. Joint Trajectory and Resource Allocation Optimization for Air-ground Collaborative Integrated Sensing and Communication Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230716

Citation:

ZHANG Guangchi, GU Zelin, CUI Miao. Joint Trajectory and Resource Allocation Optimization for Air-ground Collaborative Integrated Sensing and Communication Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230716

Citation:

PDF( 3028 KB)

Joint Trajectory and Resource Allocation Optimization for Air-ground Collaborative Integrated Sensing and Communication Systems

doi: 10.11999/JEIT230716

School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China

Funds: The Science and Technology Plan Project of Guangdong Province (2023A0505050127, 2022A0505050023, 2022A0505020008), The Guangdong Basic and Applied Basic Research Foundation Project (2023A1515011980), The Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC[2023]24)

Received Date: 2023-07-18
Rev Recd Date: 2024-01-31

Available Online: 2024-02-16

Abstract

Abstract

An air-ground collaborative integrated sensing and communication system is studied, where the air-ground collaborative network is composed of an Unmanned Ground Vehicle (UGV) base station and Unmanned Aerial Vehicle (UAV) relays. The network provides communication service for ground users while detecting and sensing target areas. The air-ground channels are modeled as the accurate Rician fading channel model. On the constraints of the sensing frequency and the effective sensing power threshold of the target areas, the minimum average communication rate of all users is maximized by jointly optimizing the communication and sensing association of the system, the transmit power and flight trajectory of the UAV relays, as well as the transmit power and trajectory of the UGV base station. To solve the resultant non-convex integer optimization problem with highly coupled variables, the block coordinate descent method is applied to decompose the original optimization problem into four sub-problems, where relaxation variables are introduced, and the integer constraints are converted into penalty terms. Then, it is proved that the effective sensing power is a composition function of the trajectory variables and the relaxation variables and is a jointly convex function of them, so that the non-convex terms are tackled by using the successive convex optimization method. Lastly, a two-layer iterative algorithm is proposed to obtain the suboptimal solution efficiently. It is showed by simulation results that as compared to some benchmark algorithms, the proposed algorithm significantly increases the minimum average communication rate of all users while achieving the same sensing performance and achieves a better performance trade-off between communication and sensing with good convergence performance.
- Integrated sensing and communication,
- Air-ground collaboration,
- Resource allocation,
- Trajectory optimization

FullText(HTML)

References(18)

References

[1]	LIU Fan, CUI Yuanhao, MASOUROS C, et al. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(6): 1728–1767. doi: 10.1109/JSAC.2022.3156632
[2]	CUI Yuanhao, LIU Fan, JING Xiaojun, et al. Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges[J]. IEEE Network, 2021, 35(5): 158–167. doi: 10.1109/MNET.010.2100152
[3]	SAAD W, BENNIS M, and CHEN Mingzhe. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems[J]. IEEE Network, 2020, 34(3): 134–142. doi: 10.1109/MNET.001.1900287
[4]	ZHANG J A, RAHMAN M L, WU Kai, et al. Enabling joint communication and radar sensing in mobile networks—a survey[J]. IEEE Communications Surveys & Tutorials, 2022, 24(1): 306–345. doi: 10.1109/COMST.2021.3122519
[5]	LIU Tianyu, CUI Miao, ZHANG Guangchi, et al. 3D trajectory and transmit power optimization for UAV-enabled multi-link relaying systems[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(1): 392–405. doi: 10.1109/TGCN.2020.3048135
[6]	WANG Xinyi, FEI Zesong, 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
[7]	LYU Zhonghao, ZHU Guangxu, and XU Jie. 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
[8]	MENG Kaitao, WU Qingqing, MA Shaodan, 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
[9]	WU Jun, YUAN Weijie, and BAI Lin. On the interplay between sensing and communications for UAV trajectory design[J]. IEEE Internet of Things Journal, 2023, 10(23): 20383–20395. doi: 10.1109/JIOT.2023.3287991
[10]	DENG Cailian, FANG Xuming, and WANG Xianbin. 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
[11]	YOU Changsheng and ZHANG Rui. 3D trajectory optimization in rician fading for UAV-enabled data harvesting[J]. IEEE Transactions on Wireless Communications, 2019, 18(6): 3192–3207. doi: 10.1109/TWC.2019.2911939
[12]	QIU Chen, WEI Zhiqing, FENG Zhiyong, et al. Joint resource allocation, placement and user association of multiple UAV-mounted base stations with in-band wireless backhaul[J]. IEEE Wireless Communications Letters, 2019, 8(6): 1575–1578. doi: 10.1109/LWC.2019.2928544
[13]	WU Silei, XU Wenjun, WANG Fengyu, et al. Distributed federated deep reinforcement learning based trajectory optimization for air-ground cooperative emergency networks[J]. IEEE Transactions on Vehicular Technology, 2022, 71(8): 9107–9112. doi: 10.1109/TVT.2022.3175592
[14]	ZHOU Yi, CHENG Nan, LU Ning, et al. Multi-UAV-aided networks: Aerial-ground cooperative vehicular networking architecture[J]. IEEE Vehicular Technology Magazine, 2015, 10(4): 36–44. doi: 10.1109/MVT.2015.2481560
[15]	KANG Zhenyu, YOU Changsheng, and ZHANG Rui. 3D placement for multi-UAV relaying: An iterative Gibbs-sampling and block coordinate descent optimization approach[J]. IEEE Transactions on Communications, 2021, 69(3): 2047–2062. doi: 10.1109/TCOMM.2020.3043776
[16]	ISKANDAR and SHIMAMOTO S. The channel characterization and performance evaluation of mobile communication employing stratospheric platform[C]. IEEE/ACES International Conference on Wireless Communications and Applied Computational Electromagnetics, Honolulu, USA, 2005: 828–831.
[17]	MENG Kaitao, WU Qingqing, MA Shaodan, et al. Throughput maximization for UAV-enabled integrated periodic sensing and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(1): 671–687. doi: 10.1109/TWC.2022.3197623
[18]	BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004.