多天线无人机视频通信中的节能资源分配策略

doi:10.19678/j.issn.1000-3428.0069356

摘要/Abstract

摘要：

凭借移动便捷、部署灵活的特点, 无人机(UAV)通信成为满足下一代蜂窝用户需求的有效技术。利用UAV捕获视频并通过边缘计算对视频进行处理, 可以为多用户提供高质量的视频服务。考虑一种多天线固定翼UAV视频传输系统, 通过合理规划UAV轨迹, 利用波束成形技术将每个传输信号引导到至用户端的最佳路径上, 从而降低能耗并提高接收信号的强度。将上述要求建模为一个非凸问题, 这个问题需要联合优化UAV轨迹、飞行时间、传输波束成形以及计算资源分配, 从而在满足用户服务质量(QoS)要求的同时, 最小化UAV总能耗。为了解决该问题, 提出一种两阶段算法: 在第一阶段, 利用路径离散化方法、交替优化技术以及连续凸逼近(SCA)技术, 最小化UAV的推进能耗; 在第二阶段, 采用路径离散化方法和SCA技术来最小化UAV的通信和计算能耗。仿真结果表明, 相较于基准方案, 该算法在保证视频质量的同时, 能够明显降低UAV的能耗, 具有很高的效率和实用性。

关键词: 无人机, 多天线, 视频流, 边缘计算, 服务质量, 能耗

Abstract:

The convenient mobility and flexible deployment of Unmanned Aerial Vehicle (UAV) communication have made it an effective technology for meeting the needs of next-generation cellular users. Using UAV to capture and process videos through edge computing can provide high-quality video services for multiple users. This paper considers a multi-antenna fixed-wing UAV video transmission system. By reasonably planning the UAV trajectory, beamforming technology is used to guide each transmission signal to the best path on the user side, thereby reducing energy consumption and improving the strength of the received signal. The above requirements are modeled as a nonconvex problem that requires the joint optimization of the UAV trajectory, flight time, transmission beamforming, and computing resource allocation to minimize the total energy consumption of the UAV while meeting user Quality of Service (QoS) requirements. To solve this problem, a two-stage algorithm is proposed. In the first stage, path dispersion, alternating optimization technology, and Continuous Convex Approximation (SCA) technology are used to minimize the driving energy consumption of the UAV. In the second stage, the path discrete method and SCA technology are used to minimize UAV communication and computing energy consumption. Simulation results show that, compared with the benchmark scheme, the proposed algorithm significantly reduces the energy consumption of the UAV while ensuring video quality, and it is highly efficient and practical.

Key words: Unmanned Aerial Vehicle (UAV), multi-antennas, video streaming, edge computing, Quality of Service (QoS), energy consumption

杨莉斌, 詹成, 李婷婷, 廖婧睿. 多天线无人机视频通信中的节能资源分配策略[J]. 计算机工程, 2025, 51(6): 266-274.

YANG Libin, ZHAN Cheng, LI Tingting, LIAO Jingrui. Energy-Efficient Resource Allocation Strategies in Multi-Antenna UAV Video Communication[J]. Computer Engineering, 2025, 51(6): 266-274.

https://www.ecice06.com/CN/Y2025/V51/I6/266

图/表 4

图1 在不同初始速度下经过优化后的UAV飞行轨迹

Fig.1 Optimized UAV flight path under different initial speeds

图2 直线轨迹与优化后轨迹的推进能耗对比

Fig.2 Comparison of propulsion energy consumption between linear trajectory and optimized trajectory

图3 在不同视频数据量下的通信和计算能耗对比

Fig.3 Comparison of communication and computing energy consumption under different video data volumes

图4 在每比特所需的CPU周期数不同条件下的通信和计算能耗对比

Fig.4 Comparison of communication and computing energy consumption under different CPU cycles per bit

参考文献 26

1	WANG Z , ZHENG J . Performance modeling and analysis of base station cooperation for cellular-connected UAV networks. IEEE Transactions on Vehicular Technology, 2022, 71 (2): 1807- 1819. doi: 10.1109/TVT.2021.3123826
2	WU J H , SUN Y N , LI D Y , et al. An adaptive conversion speed Q-Learning algorithm for search and rescue UAV path planning in unknown environments. IEEE Transactions on Vehicular Technology, 2023, 72 (12): 15391- 15404. doi: 10.1109/TVT.2023.3297837
3	HARIKUMAR K , SENTHILNATH J , SUNDARAM S . Multi-UAV oxyrrhis marina-inspired search and dynamic formation control for forest firefighting. IEEE Transactions on Automation Science and Engineering, 2019, 16 (2): 863- 873. doi: 10.1109/TASE.2018.2867614
4	DANG T , LIU C X , PENG M G . Low-latency mobile virtual reality content delivery for unmanned aerial vehicle-enabled wireless networks with energy constraints. IEEE Transactions on Vehicular Technology, 2023, 72 (2): 2189- 2201. doi: 10.1109/TVT.2022.3212868
5	田贤忠, 闵旭, 周璐. 无人机辅助的服务缓存边缘计算最优计算卸载决策与资源分配. 小型微型计算机系统, 2023, 44 (7): 1557- 1562.
	TIAN X Z , MIN X , ZHOU L . UAV-enabled service caching edge computing optimal computation floading and resource allocation strategy. Journal of Chinese Computer Systems, 2023, 44 (7): 1557- 1562.
6	邝祝芳, 陈清林, 李林峰, 等. 基于深度强化学习的多用户边缘计算任务卸载调度与资源分配算法. 计算机学报, 2022, 45 (4): 812- 824.
	KUANG Z F , CHEN Q L , LI L F , et al. Multi-user edge computing task offloading scheduling and resource allocation based on deep reinforcement learning. Chinese Journal of Computers, 2022, 45 (4): 812- 824.
7	YAN H , YANG S H , CHEN Y F , et al. Optimum battery weight for maximizing available energy in UAV-enabled wireless communications. IEEE Wireless Communications Letters, 2021, 10 (7): 1410- 1413. doi: 10.1109/LWC.2021.3069078
8	YAN H , CHEN Y F , YANG S H . New energy consumption model for rotary-wing UAV propulsion. IEEE Wireless Communications Letters, 2021, 10 (9): 2009- 2012. doi: 10.1109/LWC.2021.3090772
9	HUANG H L , SAVKIN A . Navigating UAVs for optimal monitoring of groups of moving pedestrians or vehicles. IEEE Transactions on Vehicular Technology, 2021, 70 (4): 3891- 3896. doi: 10.1109/TVT.2021.3065102
10	EL-MALEK A H A , ABOULHASSAN M A , SALHAB A M , et al. Performance analysis and optimization of UAV-assisted networks: single UAV with multiple antennas versus multiple UAVs with single antenna. IEEE Systems Journal, 2023, 17 (3): 3468- 3479. doi: 10.1109/JSYST.2023.3279335
11	WANG J X , HAN R , BAI L , et al. Coordinated beamforming for UAV-aided millimeter-wave communications using GPML-based channel estimation. IEEE Systems Journal, 2021, 7 (1): 100- 109.
12	LIU C H , LIANG D C , SYED M A , et al. A 3D tractable model for UAV-enabled cellular networks with multiple antennas. IEEE Transactions on Wireless Communications, 2021, 20 (6): 3538- 3554. doi: 10.1109/TWC.2021.3051415
13	XIAO Z Y , ZHU L P , LIU Y M , et al. A survey on millimeter-wave beamforming enabled UAV communications and networking. IEEE Communications Surveys & Tutorials, 2021, 24 (1): 557- 610.
14	LIU J Y , SHENG M , LYU R L , et al. Access points in the air: modeling and optimization of fixed-wing UAV network. IEEE Journal on Selected Areas in Communications, 2020, 38 (12): 2824- 2835. doi: 10.1109/JSAC.2020.3005496
15	ZHAN C , ZENG Y , ZHANG R . Energy-efficient data collection in UAV enabled wireless sensor network. IEEE Wireless Communications Letters, 2018, 7 (3): 328- 331. doi: 10.1109/LWC.2017.2776922
16	李斌, 彭思聪, 费泽松. 基于边缘计算的无人机通感融合网络波束成形与资源优化. 通信学报, 2023, 44 (9): 228- 237.
	LI B , PENG S C , FEI Z S . Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing. Journal on Communications, 2023, 44 (9): 228- 237.
17	UBIK S , POSPISILIK J . Video camera latency analysis and measurement. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31 (1): 140- 147. doi: 10.1109/TCSVT.2020.2978057
18	纪澎善, 贾向东, 路艺, 等. 无人机协助的多用户MIMO毫米波网络混合预编码方案. 计算机工程, 2021, 47 (8): 183- 188. URL
	JI P S , JIA X D , LU Y , et al. Hybrid precoding scheme of UAV-assisted multi-user MIMO millimeter wave network. Computer Engineering, 2021, 47 (8): 183- 188. URL
19	ZENG Y , ZHANG R . Energy-efficient UAV communication with trajectory optimization. IEEE Transactions on Wireless Communications, 2019, 18 (4): 3747- 3760.
20	ZHAN C , ZENG Y . Energy minimization for cellular-connected UAV: from optimization to deep reinforcement learning. IEEE Transactions on Wireless Communications, 2022, 21 (7): 5541- 5555. doi: 10.1109/TWC.2022.3142018
21	ZENG Y , XU J , ZHANG R . Energy minimization for wireless communication with rotary-wing UAV. IEEE Transactions on Wireless Communications, 2019, 18 (4): 2329- 2344. doi: 10.1109/TWC.2019.2902559
22	Stephen Boyd, CVX: MATLAB software for disciplined convex programming[EB/OL]. [2023-10-05]. https://cvxr.com/cvx.
23	LIAO J R, ZHAN C, YANG Y, et al. QoE maximization for multi-antenna UAV-enabled video streaming[EB/OL]. [2023-10-05]. https://ieeexplore.ieee.org/document/10001324.
24	LIAO J R , ZHAN C , ZENG B , et al. Energy-efficient optimization for IRS-enabled multiantenna UAV video streaming. IEEE Internet of Things Journal, 2024, 11 (6): 9522- 9535. doi: 10.1109/JIOT.2023.3323308
25	ZHAN C , HU H , SUI X F , et al. Joint resource allocation and 3D aerial trajectory design for video streaming in UAV communication systems. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31 (8): 3227- 3241. doi: 10.1109/TCSVT.2020.3035618
26	ZHANG J W , ZENG Y , ZHANG R . Multi-antenna UAV data harvesting: joint trajectory and communication optimization. Journal of Communications and Information Networks, 2020, 5 (1): 86- 99. doi: 10.23919/JCIN.2020.9055113

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