AoI-prioritized Multi-UAV Deployment and Resource Allocation Method in Scenarios with Differentiated User Requirements
-
摘要: 在发生自然灾害等紧急情况下，地面固定基站被损毁，可能无法及时恢复。同时，由于无人机的灵活性和低成本特性，基于无人机的应急通信需求吸引了学术界和工业界的广泛关注。然而，在探索应急通信中的带宽和功率分配方案时，现有的方案忽略了不同地面用户之间业务量需求的差异性，同时也未充分考虑信息新鲜度对应急决策的重要性。考虑到不同用户的业务量需求，且信息年龄(Age of Information, AoI)直接影响应急响应的时效性，该文提出了一种用于应急场景下的基于AoI的多无人机部署及资源分配方案。首先，在满足用户总业务量需求下，求解所需最少无人机数量。然后，进一步优化无人机的带宽、功率和三维位置，以最小化系统的平均AoI。仿真结果表明，所提出的方案在保证AoI最小的同时，所需的无人机数量最少。此外，与未联合优化无人机位置及通信资源的基准方案相比，该文所提方案显著提升了信息新鲜度，使系统平均AoI降低了21.1%。Abstract:
Objective In emergency scenarios such as natural disasters, ground-based fixed base stations are often damaged and may not be restored promptly. Meanwhile, due to the flexibility and low-cost characteristics of Unmanned Aerial Vehicles (UAVs), UAV-assisted emergency communication has attracted widespread attention from academia and industry. However, when exploring bandwidth and power allocation schemes for emergency communications, existing approaches often overlook the heterogeneity of traffic demands among different ground users (GUs) and fail to fully account for the importance of information freshness for emergency decision-making. Considering the differentiated traffic requirements of users and the direct impact of Age of Information (AoI) on the timeliness of emergency response, this paper proposes an AoI-based Joint UAV Deployment and Resource Allocation scheme for emergency scenarios. The primary objectives are: (1) to determine the minimum number of UAVs required while satisfying the total traffic demand of GUs, and (2) to jointly optimize the bandwidth, power, and three-dimensional (3D) positions of UAVs to minimize the system's average AoI, thereby ensuring timely and fresh information delivery in critical situations. Methods To achieve the research objective, a two-stage method combining the Multiple UAV Deployment (MUD) algorithm and the Bandwidth, Power, and 3D Location (BPL) joint optimization algorithm is proposed, with specific steps as follows: Determination of minimum UAV quantity: The Particle Swarm Optimization (PSO) algorithm is used to calculate the traffic density of each uncovered GU. The GU with the maximum traffic density is selected as the core, and its adjacent GUs are included in a cluster. The PSO algorithm optimizes the cluster’s position to maximize the covered traffic volume while satisfying UAV service constraints, thereby determining the minimum number of UAVs required. Joint optimization of resources and UAV positions: The BPL algorithm is adopted to optimize bandwidth, power, and 3D positions of UAVs. For bandwidth allocation, an improved relaxation adjustment scheme is used: weights are assigned based on GUs’ data transmission time, and subchannels are dynamically allocated to balance transmission time. Power allocation follows the same logic as bandwidth allocation. For 3D position optimization, the Whale Optimization Algorithm (WOA) is applied: after fixing the UAV’s 2D horizontal position, the minimum height required for coverage is derived based on ellipse characteristics to reduce energy consumption, converting the 3D optimization problem into a 2D one to find the optimal position. Results and Discussions Simulation results demonstrate the effectiveness and superiority of the proposed scheme. In a scenario with 100 GUs randomly distributed in a 1 km × 1 km area, 7 UAVs are required to achieve a coverage rate of 90% ( Fig. 3 ). The system's average AoI under this deployment is computed, meeting the basic real-time requirements for emergency communications. Compared with benchmark algorithms such as Weighted K-Means (WKM) and Minimum Degree Prior (MDP), the proposed MUD algorithm consistently requires the fewest UAVs under varying conditions of area size, GU count, and UAV service capability (Fig. 4 ). Analysis shows that as the maximum traffic demand per GU increases, the data transmission time rises, necessitating more UAVs, while the UAV climbing time decreases due to smaller cluster radii; overall, the average AoI experiences a slight decrease (Fig. 5 ).The proposed improved resource allocation scheme significantly outperforms average allocation. It reduces the maximum GU data transmission time by 26.35% (Fig. 6 ) and allocates 16.7% more bandwidth and power to high-traffic GUs (Fig. 7 ), leading to more balanced transmission times and higher resource utilization. Comparisons with three benchmark schemes—NBPL (no optimization), OL (only location optimization), and OBP (only bandwidth-power optimization)—show that the full BPL algorithm achieves the lowest average AoI across different GU counts. Specifically, when the number of GUs is large, the BPL algorithm reduces the average AoI by approximately 21.1% compared to the NBPL scheme (Fig. 8 ). Furthermore, the proposed scheme maintains the lowest total energy consumption per UAV among the compared methods (Fig. 9 ), validating its energy efficiency. The algorithm's computational complexity is analyzed to be manageable for practical emergency deployment.Conclusions This paper proposes an AoI-prioritized multi-UAV deployment and resource allocation scheme for emergency communication scenarios characterized by differentiated user traffic demands. The scheme integrates a PSO-enhanced MUD algorithm to determine the minimum UAV count and a BPL algorithm that jointly optimizes bandwidth, power, and 3D positions using WOA and an improved resource allocation strategy. The proposed method simultaneously achieves three key objectives: minimizing the number of deployed UAVs, minimizing the system's average AoI to ensure information freshness, and reducing the total energy consumption. Simulation results confirm its superiority over existing benchmarks in terms of deployment efficiency, AoI performance, and energy efficiency. The work provides a reliable and efficient technical solution for UAV-assisted emergency communications. Future research directions include extending the model to non-LoS channel conditions, developing lower-complexity heuristic algorithms for larger-scale scenarios, designing distributed optimization frameworks, and investigating online joint trajectory and resource optimization strategies for dynamic environments. -
[1] 陈佳美, 孙慧雯, 李玉峰, 等. 基于双深度Q网络算法的无人机辅助密集网络资源优化策略[J]. 电子与信息学报, 2025, 47(8): 2621–2629. doi: 10.11999/JEIT250021.CHEN Jiamei, SUN Huiwen, LI Yufeng, et al. Double deep Q network algorithm-based unmanned aerial vehicle-assisted dense network resource optimization strategy[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2621–2629. doi: 10.11999/JEIT250021. [2] LI Huan, TANG Xiao, ZHAI Daosen, et al. Energy-efficient deployment and resource allocation for O-RAN-enabled UAV-assisted communication[J]. IEEE Transactions on Green Communications and Networking, 2024, 8(3): 1128–1140. doi: 10.1109/TGCN.2024.3422393. [3] YANG K, YU J, and LEE C. Base station deployment for path-aware UAV communications[C]. 2024 International Conference on Electronics, Information, and Communication (ICEIC), Taipei, Taiwan, 2024: 1–4. doi: 10.1109/ICEIC61013.2024.10457175. [4] NOMIKOS N, GKONIS P K, BITHAS P S, et al. A survey on UAV-aided maritime communications: Deployment considerations, applications, and future challenges[J]. IEEE Open Journal of the Communications Society, 2023, 4: 56–78. doi: 10.1109/OJCOMS.2022.3225590. [5] GU Xiaohui and ZHANG Guoan. A survey on UAV-assisted wireless communications: Recent advances and future trends[J]. Computer Communications, 2023, 208: 44–78. doi: 10.1016/j.comcom.2023.05.013. [6] SHI Guoliang, QIU Dongxing, and HU Qiliang. Three-dimensional position deployment of UAV base stations based on improved grey wolf optimization algorithm[C]. 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, 2024: 997–1002. doi: 10.1109/ICETCI61221.2024.10594358. [7] 何江, 喻莞芯, 黄浩, 等. 多无人机分布式感知任务分配-通信基站关联与飞行策略联合优化设计[J]. 电子与信息学报, 2025, 47(5): 1402–1417.HE Jiang, YU Wanxin, HUANG Hao, et al. Joint task allocation, communication base station association and flight strategy optimization design for distributed sensing unmanned aerial vehicles[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1402–1417. [8] 李威, 李跃军. 利用无人机搭建高空基站的研究[J]. 通讯世界, 2017(9): 12–13. doi: 10.3969/j.issn.1006-4222.2017.09.007.LI Wei and LI Yuejun. The research on building high-altitude communication base station by using UAV[J]. Telecom World, 2017(9): 12–13. doi: 10.3969/j.issn.1006-4222.2017.09.007. [9] ROTTONDI C, MALANDRINO F, BIANCO A, et al. Scheduling of emergency tasks for multiservice UAVs in post-disaster scenarios[J]. Computer Networks, 2021, 184: 107644. doi: 10.1016/j.comnet.2020.107644. [10] JIN Nansen, GUI Jinsong, and ZHOU Xinran. Equalizing service probability in UAV-assisted wireless powered mmWave networks for post-disaster rescue[J]. Computer Networks, 2023, 225: 109644. doi: 10.1016/j.comnet.2023.109644. [11] YANG Peng, CAO Xianbin, YIN Chao, et al. Proactive drone-cell deployment: Overload relief for a cellular network under flash crowd traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(10): 2877–2892. doi: 10.1109/TITS.2017.2700432. [12] PRASAD N L and RAMKUMAR B. 3-D deployment and trajectory planning for relay based UAV assisted cooperative communication for emergency scenarios using Dijkstra's algorithm[C]. IEEE Transactions on Vehicular Technology, 2023, 72(4): 5049–5063. doi: 10.1109/TVT.2022.3224304. [13] ZHANG Xuhui, XING Huijun, SHEN Yanyan, et al. Age of information minimization in UAV-enabled IoT networks via federated reinforcement learning[J]. IEEE Transactions on Wireless Communications, 2025, 24(9): 7923–7939. doi: 10.1109/TWC.2025.3563426. [14] ZHANG Shuhang, ZHANG Hongliang, HAN Zhu, et al. Age of information in a cellular internet of UAVs: Sensing and communication trade-off design[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6578–6592. doi: 10.1109/TWC.2020.3004162. [15] ABD-ELMAGID M A and DHILLON H S. Average peak age-of-information minimization in UAV-assisted IoT networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 2003–2008. doi: 10.1109/TVT.2018.2885871. [16] ZHU Botao, BEDEER E, NGUYEN H H, et al. UAV trajectory planning for AoI-minimal data collection in UAV-aided IoT networks by transformer[J]. IEEE Transactions on Wireless Communications, 2023, 22(2): 1343–1358. doi: 10.1109/TWC.2022.3204438. [17] ZHANG Jianhang, KANG Kai, YANG Miao, et al. AoI-minimization in UAV-assisted IoT network with massive devices[C]. 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, USA, 2022: 1290–1295. doi: 10.1109/WCNC51071.2022.9771857. [18] WAN Liangtian, ZHANG Kun, SUN Lu, et al. Energy-AOI-aware UAV-assisted data collection: A multi-agent deep reinforcement learning-based trajectory optimization[C]. 2023 IEEE 23rd International Conference on Communication Technology (ICCT), Wuxi, China, 2023: 176–180. doi: 10.1109/ICCT59356.2023.10419500. [19] KALANTARI E, YANIKOMEROGLU H, and YONGACOGLU A. On the number and 3D placement of drone base stations in wireless cellular networks[C]. 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, Canada, 2016: 1–6. doi: 10.1109/VTCFall.2016.7881122. [20] LUO Jingjing, SONG Jialun, ZHENG Fuchun, et al. User-centric UAV deployment and content placement in cache-enabled multi-UAV networks[J]. IEEE Transactions on Vehicular Technology, 2022, 71(5): 5656–5660. doi: 10.1109/TVT.2022.3152246. [21] WU Di, XU Juan, YUAN Jiabin, et al. A novel deployment method for UAV-mounted mobile base stations[C]. 2021 17th International Conference on Mobility, Sensing and Networking (MSN), Exeter, United Kingdom, 2021: 40–47. doi: 10.1109/MSN53354.2021.00022. [22] ALZENAD M, EL-KEYI A, LAGUM F, et al. 3-D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage[J]. IEEE Wireless Communications Letters, 2017, 6(4): 434–437. doi: 10.1109/LWC.2017.2700840. [23] 李莹雪, 赵继军, 魏忠诚. 基于无人机基站的节能通信部署研究[J]. 计算机应用与软件, 2022, 39(5): 153–159. doi: 10.3969/j.issn.1000-386x.2022.05.024.LI Yingxue, ZHAO Jijun, and WEI Zhongcheng. Energy-efficient deployment of unmanned aerial vehicle base station[J]. Computer Applications and Software, 2022, 39(5): 153–159. doi: 10.3969/j.issn.1000-386x.2022.05.024. [24] MOZAFFARI M, SAAD W, BENNIS M, et al. Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage[J]. IEEE Communications Letters, 2016, 20(8): 1647–1650. doi: 10.1109/LCOMM.2016.2578312. [25] NOH S C, JEON H B, and CHAE C B. Energy-efficient deployment of multiple UAVs using ellipse clustering to establish base stations[J]. IEEE Wireless Communications Letters, 2020, 9(8): 1155–1159. doi: 10.1109/LWC.2020.2982889. [26] GUO Hongying, WANG Li, LI Ruoguang, et al. An improved virtual force approach for UAV deployment and resource allocation in emergency communications[Z]. arXiv: 2401.09013, 2024. doi: 10.48550/arXiv.2401.09013. (查阅网上资料,不确定文献类型及格式是否正确,请确认). [27] ZENG Yong, XU Jie, and ZHANG Rui. Energy minimization for wireless communication with rotary-wing UAV[J]. IEEE Transactions on Wireless Communications, 2019, 18(4): 2329–2345. doi: 10.1109/TWC.2019.2902559. [28] YOU J S, JUNG S, SEO J, et al. Energy-efficient 3-D placement of an unmanned aerial vehicle base station with antenna tilting[J]. IEEE Communications Letters, 2020, 24(6): 1323–1327. doi: 10.1109/LCOMM.2020.2979437. [29] CROSWELL W. Antenna theory; analysis and design[J]. IEEE Antennas and Propagation Society Newsletter, 1982, 24(6): 28–29. doi: 10.1109/MAP.1982.27654. [30] MIRJALILI S and LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51–67. doi: 10.1016/j.advengsoft.2016.01.008. [31] LIANG Tianhao, ZHANG Tingting, WU Qingqing, et al. Age of information based scheduling for UAV aided localization and communication[J]. IEEE Transactions on Wireless Communications, 2024, 23(5): 4610–4626. doi: 10.1109/TWC.2023.3320871. -
下载:
图(6)
计量
- 文章访问数: 9
- HTML全文浏览量: 4
- PDF下载量: 0
- 被引次数: 0
下载:
