Resource Allocation and Node Deployment for Multi-UAV Integrated Localization and Communication with Differentiated Requirements
-
摘要: 面向地面网络覆盖薄弱和全球导航卫星系统受限环境中智能终端的差异化通信定位需求，该文研究了无地面基站场景下无人机网络通信定位一体化服务的资源分配与节点部署问题。具体而言，如何通过无人机网络的资源分配与节点部署，最大程度满足终端的差异化通信定位服务需求。由于网络中物理资源被通信与定位服务共享且节点部署位置同时影响数据传输与协同定位，导致上述问题高度耦合且具有海量状态空间，较难求解。该文综合考虑终端在传输数据量、最低通信速率、定位精度门限等方面的差异以及终端服务优先级的不同，构建依赖无人机拓扑的网络服务价值模型。针对资源分配与节点部署耦合形成的混合非凸问题，提出一种网络服务价值驱动的迭代优化算法：在每轮迭代中，首先针对当前节点部署构造服务级连续资源代理，以分段一维搜索和价格二分交替优化时频资源，并将连续解恢复为原问题的可行解；随后依据资源响应生成结构化部署候选，经代理排序与束搜索筛选后，对保留的候选部署在新的服务关系下执行资源响应，以网络服务价值作为新部署及其对应资源分配结果的验收依据，直至无候选部署能够带来超过阈值的改进从而结束迭代。仿真结果表明，所提方法能够有效提高无人机网络的服务价值，并在双功能服务之间实现面向终端需求的资源权衡。Abstract:
Objective In areas where ground coverage is weak or Global Navigation Satellite System access is poor, multiple dual-functional unmanned aerial vehicles (UAVs) can offer uplink service and cooperative positioning. Ground terminals differ in data volume, minimum rate, localization-accuracy threshold, priority, and communication-localization preference. Maximizing rate, localization accuracy, or their weighted sum cannot directly reflect these requirements: a service below its activation threshold may be unusable, while resources assigned after demand saturation add little value. This study considers a quasi-static service period with prior terminal positions, formulates a topology-dependent value of service (VoS), and jointly optimizes physical resource block scheduling and UAV positions. Methods Terminals transmit data or sounding reference signals over assigned physical resource blocks. Synchronized UAVs form time-difference-of-arrival measurements, while a probabilistic air-to-ground channel determines communication access and valid localization anchors. Communication VoS combines a sigmoidal rate utility with data completeness; localization VoS maps the position error bound from the accumulated Fisher information matrix to an exponential utility. Priorities and service preferences aggregate both values. The resulting problem P0 couples binary scheduling, non-concave utilities, time-frequency resources, service relationships, localization geometry, and UAV positions. For a fixed topology, average resource-block occupation and active slots form a continuous service profile that approximates, but does not upper-bound, the original schedule. Bandwidth responses are obtained by deterministic interval search with damped Newton refinement and shadow-price bisection; slot responses compare finite service-boundary and integer candidates. A floor-ceiling neighborhood and deterministic slot packing recover an integer schedule subject to capacity and terminal-power constraints. Failed packing rolls back the least valuable upward-rounded component, and VoS is recomputed from the recovered schedule, so reported schedules remain feasible for P0 without claiming global optimality. The recovered response then drives deployment. Coverage-repair, localization-geometry-improvement, and resource-saving candidates are generated from service deficits, relationships, and resource consumption. Normalized proxies, per-UAV truncation, and beam search limit complete evaluations. For each retained deployment, access relationships, anchors, Fisher information, and resource allocation are recomputed. A candidate is accepted only if its VoS gain exceeds the threshold, yielding a monotone bounded outer sequence that terminates within the generated candidate set. Resource allocation and deployment stay coupled throughout: for each retained topology, the algorithm rebuilds communication access, localization anchors, Fisher information, and the feasible integer schedule before computing VoS. Proxies only rank candidates, whereas final acceptance always uses this complete response. Thus, distance or geometry alone never selects the next topology; the accepted update reflects terminal demand, resource scarcity, and localization geometry under the same feasible scheduling constraints used by the objective. This design keeps the objective and final decision aligned, unlike a geometry-only deployment rule. Results and Discussions Simulations use a 700 m by 700 m area, four UAVs at a baseline height of 150 m, and equal proportions of communication-dominant, localization-dominant, and balanced terminals. Curves share 30 independent scenarios across methods; the height experiment reports a 95% confidence interval from 1000 scenario-level bootstrap resamples. For fair tests, all allocators share topology, resources, and random scenarios; all deployment methods use the same the proposed joint resource-allocation response, start cold, and share one cap on full response tests, with all setup and training costs included. VoS-driven allocation produces smaller communication-rate and localization-accuracy demand deviations than communication-priority and average-utility objectives because saturation redirects resources from over-provisioned to insufficiently served requests (Fig. 2 ). In the convergence test, three feasible initializations reach similar system values. Damping suppresses oscillation near non-concave segment transitions and reduces the normalized local-response residual to the stopping threshold. For small terminal sets, the inset enumerates integer service profiles under the same aggregated model; hence the reported small-scale gap is to an enumerated integer service-profile benchmark, not to exhaustive optimization of the original per-resource-block P0. Its small magnitude indicates limited empirical loss from the continuous response and integer recovery (Fig. 3 ). Under increasing load, the proposed joint allocator outperforms particle-swarm slot, non-joint, learning-based, weight-driven, and random alternatives (Fig. 4 ). Communication is more load-sensitive because both rate and data completeness must be maintained, whereas localization accumulates information across anchors and slots. Resource-pool tests further show that added bandwidth gives little benefit when slots are scarce, while sounding-reference-signal bandwidth more directly improves localization. The structured deployment search attains higher VoS with shorter end-to-end deployment time than particle swarm optimization, differential evolution, and Bayesian optimization by concentrating complete resource responses on service-aware candidates (Fig. 5 ). VoS first increases and then decreases with UAV height as line-of-sight improvement competes with propagation loss; horizontal updates balance communication proximity and localization-anchor diversity (Fig. 6 ).Conclusions The framework evaluates communication and localization through demand satisfaction and coordinates time-frequency scheduling with iterative UAV-topology updates by combining recovered resource responses with structured candidate search. It improves demand matching, system VoS, deployment efficiency, and interpretability while preserving feasibility for the original scheduling constraints. The model assumes prior terminal positions and a quasi-static service period; dynamic terminal movement, changing demands, and continuous UAV trajectory optimization remain for future study. -
1 服务价值驱动的资源分配与节点部署算法
输入：初始部署$ {\boldsymbol{Q}}^{(0)} $，终端请求$ {s}_{j} $，资源池$ ({K}_{f},{K}_{t}) $，最大迭
代次数$ {I}_{\text{out}} $，阈值$ \varepsilon $，候选参数$ ({K}_{q},{W}_{b},L) $输出：部署$ {\boldsymbol{Q}}^{*} $，可行排程$ {\boldsymbol{U}}^{*} $，服务价值$ V_{\text{sys}}^{*} $ (1) 初始化$ n=0 $，$ {\mathcal{G}}^{(0)}=\mathcal{G}({\boldsymbol{Q}}^{(0)}) $ (2) $ ({\boldsymbol{\xi }}^{(0)},{\boldsymbol{U}}^{(0)},V_{\text{sys}}^{(0)})=\mathcal{R}({\boldsymbol{Q}}^{(0)},{\mathcal{G}}^{(0)}) $ (3) FOR $ n=0:{I}_{\text{out}}-1 $ (4) 生成结构化候选并经代理与束搜索保留$ L $个，加入当前部署
为第0个候选，并令$ \boldsymbol{Q}_{0}^{(n)}={\boldsymbol{Q}}^{(n)} $、$ V_{0}^{(n)}=V_{\text{sys}}^{(n)} $(5) FOR $ m=1:L $ (6) $ \mathcal{G}_{m}^{(n)}=\mathcal{G}(\boldsymbol{Q}_{m}^{(n)}) $，$ (\boldsymbol{\xi }_{m}^{(n)},\boldsymbol{U}_{m}^{(n)},V_{m}^{(n)})=\mathcal{R}(\boldsymbol{Q}_{m}^{(n)},\mathcal{G}_{m}^{(n)}) $ (7) END FOR (8) $ {m}^{*}\text{=arg}{\max }_{m\in\{0,1,\cdots,L\}}V_{m}^{(n)} $ (9) IF$ V_{{m}^{*}}^{(n)}>V_{\text{sys}}^{(n)}+\varepsilon $，同步更新 $ \boldsymbol{Q},\boldsymbol{\xi },\boldsymbol{U},{V}_{\text{sys}} $；ELSE
BREAK(10) END IF；END FOR 
下载: 导出CSV
表 1 仿真参数设置表
参数 数值 频域PRB数量$ {K}_{f} $ $ \left[50,300\right]\in{\mathbb{N}}^{+} $ 时域时隙数$ {K}_{t} $ $ \left[150,350\right]\in{\mathbb{N}}^{+} $ 通信数据需求$ d_{j}^{c} $ $ \left[0.8,3.5\right] $ Mbit 定位精度需求$ {\sigma }_{j} $ $ \left[0.5,10\right] $ m 单时隙业务PRB上限$ K_{j}^{{q}_{\max }} $ $ 50 $ 单服务时隙数上限$ \tau _{j}^{{q}_{max}} $ $ 30 $ 3类终端需求权重($ w_{j}^{c},w_{j}^{l} $) $ \left(0.2{,}0.8\right)、(0.5{,}0.5)、(0.8{,}0.2) $ 单PRB带宽时隙参数($ {B}_{0},{T}_{0} $) $ \left(180\;\text{kHz},1\;\text{ms}\right) $ 信道参数($ {f}_{c},{a}_{\text{e}},{b}_{\text{e}},{\eta }_{\text{LoS}},{\eta }_{\text{NLoS}} $) $ \left(2.1\;\text{GHz},9.61{,}0.16{,}1\;\text{dB},20\;\text{dB}\right) $ ToA测量模型参数($ \psi , $$ \varsigma _{}^{2} $) $ (1.2665×10 $–2 $ ,6×10 $–18$ {\text{s}}^{2}) $ 噪声与功率谱密度($ {N}_{0},{P}_{0} $) $ \left(-174\;\text{dBm/Hz},-49.5\;\text{dBm/Hz}\right) $ 最大发射功率$ {P}_{\max } $ $ 23\;\text{dBm} $ 超参数($ {I}_{in},{I}_{out},\varepsilon $) $ (30,50,10 $–3 $ ) $ Monte Carlo次数$ {N}_{MC} $ $ 30 $
下载: 导出CSV
-
[1] 新华社. 中华人民共和国国民经济和社会发展第十五个五年规划纲要[EB/OL]. https://www.gov.cn/yaowen/liebiao/202603/content_7062633.htm, 2026. (查阅网上资料,未找到本条文献英文翻译信息,请确认). [2] PAN Yu, LI Ruoguang, DA Xinyu, et al. Cooperative trajectory planning and resource allocation for UAV-enabled integrated sensing and communication systems[J]. IEEE Transactions on Vehicular Technology, 2024, 73(5): 6502–6516. doi: 10.1109/TVT.2023.3337106. [3] 孙伟皓, 王海, 秦蓁, 等. 机会无人机辅助数据收集的组网和资源分配方法[J]. 电子与信息学报, 2025, 47(5): 1381–1391. doi: 10.11999/JEIT241053.SUN Weihao, WANG Hai, QIN Zhen, et al. Networking and resource allocation methods for opportunistic UAV-assisted data collection[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1381–1391. doi: 10.11999/JEIT241053. [4] YANG Yang, CHEN Mingzhe, BLANKENSHIP Y, et al. Positioning using wireless networks: Applications, recent progress, and future challenges[J]. IEEE Journal on Selected Areas in Communications, 2024, 42(9): 2149–2178. doi: 10.1109/JSAC.2024.3423629. [5] YANG Zheyuan, BI Suzhi, and ZHANG Y J A. Deployment optimization of dual-functional UAVs for integrated localization and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(12): 9672–9687. doi: 10.1109/TWC.2023.3273159. [6] JIANG Yifan, WU Qingqing, CHEN Wen, et al. UAV-enabled integrated sensing and communication: Tracking design and optimization[J]. IEEE Communications Letters, 2024, 28(5): 1024–1028. doi: 10.1109/LCOMM.2024.3379504. [7] 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. [8] LI Biwei, WANG Xianbin, XIN Yan, et al. Value of service maximization in integrated localization and communication system through joint resource allocation[J]. IEEE Transactions on Communications, 2023, 71(8): 4957–4971. doi: 10.1109/TCOMM.2023.3280212. [9] 3GPP. TS 38.211 V17.0. 0-2022 NR Physical channels and modulation[S]. Sophia Antipolis, European Telecommunications Standards Institute (ETSI), 2022. (查阅网上资料, 未找到本条文献信息, 请确认). [10] SHEN Yuan and WIN M Z. Fundamental limits of wideband localization—part I: A general framework[J]. IEEE Transactions on Information Theory, 2010, 56(10): 4956–4980. doi: 10.1109/TIT.2010.2060110. [11] KELLY F P, MAULLOO A K, and TAN D K H. Rate control for communication networks: Shadow prices, proportional fairness and stability[J]. Journal of the Operational Research Society, 1998, 49(3): 237–252. doi: 10.1057/palgrave.jors.2600523. [12] KWON G, CONTI A, PARK H, et al. Joint communication and localization in millimeter wave networks[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1439–1454. doi: 10.1109/JSTSP.2021.3113115. [13] YANG Liu, WEI Yifei, FENG Zhiyong, et al. Deep reinforcement learning-based resource allocation for integrated sensing, communication, and computation in vehicular network[J]. IEEE Transactions on Wireless Communications, 2024, 23(12): 18608–18622. doi: 10.1109/TWC.2024.3470873. [14] 金飞鸿, 张静, 谢亚琴. 用户需求差异化场景下信息年龄优先的多无人机部署及资源分配方法[J]. 电子与信息学报, 2026, 48(1): 253–263. doi: 10.11999/JEIT251062.JIN Feihong, ZHANG Jing, and XIE Yaqin. AoI-prioritized multi-UAV deployment and resource allocation method in scenarios with differentiated user requirements[J]. Journal of Electronics & Information Technology, 2026, 48(1): 253–263. doi: 10.11999/JEIT251062. [15] XU Renjie, HUANG Zhaoke, WANG Chenwei, et al. Evolving collaborative differential evolution for dynamic multi-objective UAV path planning[J]. IEEE Transactions on Vehicular Technology, 2026, 75(5): 7456–7468. doi: 10.1109/TVT.2025.3632847. [16] BENZAGHTA M, GERACI G, LÓPEZ-PÉREZ D, et al. Cellular network design for UAV corridors via data-driven high-dimensional Bayesian optimization[J]. IEEE Transactions on Wireless Communications, 2025, 24(9): 7530–7545. doi: 10.1109/TWC.2025.3561066. -
下载:
图(6) / 表(2)
计量
- 文章访问数: 18
- HTML全文浏览量: 2
- PDF下载量: 2
- 被引次数: 0
