A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

Xu Cifu; Li Yiqing

doi:10.12052/gdutxb.250207

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A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

更新时间：2026-05-27

- A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief
- Vol. 43, Issue 2, Pages: 111-120(2026)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.250207
  CLC： TN929.5
- Published：2026
- 稿件说明：
移动端阅览
徐赐福, 李亦卿. 面向灾后救援的多无人机通感算一体化资源分配优化方法[J]. 广东工业大学学报, 2026, 43(2): 111-120.
徐赐福, 李亦卿. 面向灾后救援的多无人机通感算一体化资源分配优化方法[J]. 广东工业大学学报, 2026, 43(2): 111-120. DOI： 10.12052/gdutxb.250207.

DOI： 10.12052/gdutxb.250207

摘要

针对自然灾害导致地面通信受损、现场数据处理受限的应急救援场景，本文构建多无人机通信感知计算一体化的联合资源分配优化模型。在满足发射功率、检测可靠性、飞行速度、安全间距、计算能力及无人机–地面接入点关联等约束下，联合优化无人机轨迹、波束成形与关联关系，以最大化回传计算数据量与感知方向增益的加权效能。针对该混合整数非凸且多变量强耦合问题，本文设计交替优化算法将其分解为3个子问题。首先，在无人机与接入点的关联问题上，通过松弛二元变量并引入线性约束，将问题转化为线性规划问题，利用内点法进行高效求解。其次，在波束成形子问题中，通过半正定松弛和凸近似技术，结合高斯随机化方法，恢复秩一解，以平衡通信速率和感知方向图增益。最后，在轨迹优化子问题上，通过一阶泰勒展开对非凸速率和安全距离约束进行线性化处理，转为连续凸近似问题，便于迭代求解。仿真结果表明，在不同参数设置下，所提方法相较其他基线方案可获得更高且更均衡的通信感知计算综合效能。

Abstract

For disaster-relief scenarios where natural disasters damage terrestrial communications and constrain on-site data processing

a multi-unmanned aerial vehicle (UAV) integrated sensing

computing

and communication system is considered and a joint resource allocation optimization model formulated. Under constraints on transmit power

detection reliability

UAV speed

inter-UAV safety distance

computing capability

and UAV-access point (AP) association

the objective is to maximize the weighted computed data and the sensing directional gain by jointly optimizing UAV trajectories

transmit beamforming

and association. The formulated problem is a strongly coupled mixed-integer nonconvex program. To address it

an alternating optimization algorithm is developed to decompose the original problem into three subproblems. Firstly

the UAV-AP association is transformed into a linear program by relaxing binary variables and introducing linear constraints

and is efficiently solved via an interior-point method. Secondly

the beamforming subproblem is handled using semidefinite relaxation

and a rank-one solution is recovered through Gaussian randomization to balance the communication rate and sensing beampattern gain. Finally

the trajectory subproblem linearizes the nonconvex rate expression and safety-distance constraints using first-order Taylor expansion

leading to a sequence of convex approximation problems that can be solved iteratively. Simulation results demonstrate that

under different parameter settings

the proposed method achieves higher and more balanced sensing

computing

and communication performance than other baseline schemes.

关键词

Keywords

references

ZENG Y, ZHANG R, LIM T J. Wireless communications with unmanned aerial vehicles: opportunities and challenges[J]. IEEE Communications Magazine, 2016, 54(5): 36-42.

YANMAZ E, YAHYANEJAD S, RINNER B, et al . Drone networks: communications, coordination, and sensing[J ] . Ad Hoc Networks, 2018, 68: 1-15.

LIU X, ANSARI N. Resource allocation in UAV-assisted M2M communications for disaster rescue[J]. IEEE Wireless Communications Letters, 2019, 8(2): 580-583.

DONG J, OTA K, DONG M. UAV-based real-time survivor detection system in post-disaster search and rescue operations[J]. IEEE Journal on Miniaturization for Air and Space Systems, 2021, 2(4): 209-219.

MEYER D, HESS M, LO E, et al . UAV-based post disaster assessment of cultural heritage sites following the 2014 South Napa Earthquake[C ] //2015 Digital Heritage. Granada: IEEE, 2015: 421-424.

LI X, TAO M, YANG S, et al . AI-empowered intelligent search for path planning in UAV-assisted data collection networks[J ] . IEEE Internet of Things Journal, 2024, 11(21): 34492-34503.

LIANG Y, XU W, LIANG W, et al . Nonredundant information collection in rescue applications via an energy-constrained UAV[J ] . IEEE Internet of Things Journal, 2019, 6(2): 2945-2958.

XU X B, WANG Q, FAN C Q, et al . An aggregated edge computing resource management method for Space-Air-Ground integrated information networks[J ] . Chinese Journal of Computers, 2023, 46(4): 690-710.

ZENG Y, XU J, ZHANG R. Energy minimization for wireless communication with rotary-wing UAV[J]. IEEE Transactions on Wireless Communications, 2019, 18(4): 2329-2345.

HUANG N, DOU C, WU Y, et al . Unmanned-aerial-vehicle-aided integrated sensing and computation with mobile-edge computing[J ] . IEEE Internet of Things Journal, 2023, 10(19): 16830-16844.

WANG L, WU H, DING Y, et al . Hypergraph-based wireless dis tributed storage optimization for cellular D2D underlays[J ] . IEEE Journal on Selected Areas in Communications, 2016, 34(10): 2650-2666.

MONDAL A, MISHRA D, PRASAD G, et al . Joint trajectory, user-association, and power control for green UAV-assisted data collection using deep reinforcement learning[J ] . IEEE Transactions on Intelligent Vehicles, 2025, 10(1): 548-561.

BAI Z, LIN Y, CAO Y, et al . Delay-aware cooperative task offloading for multi-UAV enabled edge-cloud computing[J ] . IEEE Transactions on Mobile Computing, 2024, 23(2): 1034-1049.

LI B, LIU W, XIE W, et al . Adaptive digital twin for UAV-assisted integrated sensing, communication, and computation networks[J ] . IEEE Transactions on Green Communications and Networking, 2023, 7(4): 1996-2009.

LIU Y, ZHANG B, GUO D, et al . Joint precoding design and location optimization in joint communication, sensing and computing of UAV systems[J ] . IEEE Transactions on Cognitive Communications and Networking, 2024, 10(2): 541-552.

HUANG N, DOU C, WU Y, et al . Mobile edge computing aided integrated sensing and communication with short-packet transmissions[J ] . IEEE Transactions on Wireless Communications, 2024, 23(7): 7759-7774.

YI Y Q, WU Q J, CUI M, et al . Computing delay minimization for UAV-enabled mobile edge computing systems with URLLC-based offloading[J ] . Journal of Guangdong University of Technology, 2024, 41(4): 70-79.

XU Y, ZHANG T, LIU Y, et al . UAV-enabled integrated sensing, computing, and communication: a fundamental trade-off[J ] . IEEE Wireless Communications Letters, 2023, 12(5): 843-847.

YANG Y, WEI X, XU R, et al . Joint optimization of AoI, SINR, completeness, and energy in UAV-aided SDCNs: coalition formation game and cooperative order[J ] . IEEE Transactions on Green Communications and Networking, 2022, 6(1): 265-280.

SHU Y, LIAO H, YAO Z, et al . Integrated sensing, communication, and computing for self-powered UAV-assisted corona detection in high-voltage substations[J ] . IEEE Sensors Journal, 2023, 23(18): 20874-20881.

ZHANG Z, WANG S, CHEN J, et al . A bionic dynamic path planning algorithm of the micro UAV based on the fusion of deep neural network optimization/filtering and hawk-eye vision[J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(6): 3728-3740.

LI T, LENG S, WANG Z, et al . Intelligent resource allocation schemes for UAV-swarm-based cooperative sensing[J ] . IEEE Internet of Things Journal, 2022, 9(21): 21570-21582.

CAI T, YANG Z, CHEN Y, et al . Cooperative data sensing and computation offloading in UAV-assisted crowdsensing with multi-agent deep reinforcement learning[J ] . IEEE Transactions on Network Science and Engineering, 2022, 9(5): 3197-3211.

XU Y, ZHANG T, LIU Y, et al . UAV-assisted MEC networks with aerial and ground cooperation[J ] . IEEE Transactions on Wireless Communications, 2021, 20(12): 7712-7727.

MENG K, WU Q, MA S, et al . UAV trajectory and beamforming optimization for integrated periodic sensing and communication[J ] . IEEE Wireless Communications Letters, 2022, 11(6): 1211-1215.

STOICA P, JIAN LI, YAO XIE. On probing signal design for MIMO radar[J]. IEEE Transactions on Signal Processing, 2007, 55(8): 4151-4161.

LUO Z QUAN, MA W KIN, SO A, et al . Semidefinite relaxation of quadratic optimization problems[J ] . IEEE Signal Processing Magazine, 2010, 27(3): 20-34.

ZHANG G C, GU Z L, CUI M, et al . Joint trajectory and resource allocation optimization for air-ground collaborative integrated sensing and communication systems[J ] . Journal of Electronics Information Technology, 2024, 46(6): 2382-2390.

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