Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs

TAN Ling; XU Hai; LIU Yu-feng; XIA Jing-ming

doi:10.12263/DZXB.20230513

您当前的位置：

首页 >

文章列表页 >

Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs

PAPERS | 更新时间：2025-12-08

- Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3070-3078(2023)
- 作者机构：
  
  1.南京信息工程大学计算机学院,江苏南京 210044
  2.南京信息工程大学软件学院,江苏南京 210044
  3.南京信息工程大学人工智能学院,江苏南京 210044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271264)
- DOI：10.12263/DZXB.20230513
  CLC： TP391
- Received：08 June 2023，
  
  Revised：2023-09-02，
  
  Published：25 November 2023
- 稿件说明：
移动端阅览
谈玲,许海,刘玉风等.基于多无人机的空中计算网络资源分配算法[J].电子学报,2023,51(11):3070-3078.

TAN Ling,XU Hai,LIU Yu-feng,et al.Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs[J].ACTA ELECTRONICA SINICA,2023,51(11):3070-3078.
谈玲,许海,刘玉风等.基于多无人机的空中计算网络资源分配算法[J].电子学报,2023,51(11):3070-3078. DOI： 10.12263/DZXB.20230513.

TAN Ling,XU Hai,LIU Yu-feng,et al.Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs[J].ACTA ELECTRONICA SINICA,2023,51(11):3070-3078. DOI： 10.12263/DZXB.20230513.

摘要

空中计算（over-the-Air Computation，AirComp）是一种有效提升分布式数据聚合效率的方法.现有研究大多采用单无人机（Unmanned Aerial Vehicle，UAV）方案，未考虑数据聚合质量和系统稳定性.为此，本文提出一种基于多UAV辅助的AirComp网络，旨在实现多个地面移动传感器（Ground Mobile Sensor，GMS）的高效聚合.为了改进数据采集质量并全面反映系统性能，本文设计了一个多约束优化问题，通过联合优化UAV-GMS关联、UAV三维（Three Dimensional，3D）部署、UAV去噪因子以及传输功率分配，以最大化系统的最小可达速率.针对多约束优化问题的非线性特征，本文提出一种AirComp网络下多UAV辅助的深度确定性策略梯度优化算法（Deep Deterministic Policy Gradient-based optimization algorithm for multi-UAV cooperation in AirComp network，AirDDPG-UAV），用以协助多UAV在复杂环境下快速响应聚合任务.该算法利用深度强化学习的确定性策略对网络中的状态、行为和奖励进行优化，以最大化系统最小可达速率.数值结果显示，AirDDPG-UAV算法在保证较低的系统能耗和计算复杂度前提下，能够使系统最小可达速率提高15%，表明本文所提方案适用于分布式数据聚合，可以有效提高数据聚合效率.

Abstract

Over-the-air computation (AirComp) is an effective method to improve the efficiency of distributed data aggregation

which can complete some task calculations while transmitting in the air. Most existing researches focus on the single unmanned aerial vehicle (UAV) scheme

without considering the quality of data aggregation and the stability of the system

making it unsuitable for practical AirComp environments. Therefore

this paper proposes an AirComp network based on multiple UAVs collaboration

which aims to achieve the efficient data aggregation for multiple ground mobile sensors (GMSs). In order to refine data acquisition and fully reflect system status

a multi-constraint non-convex optimization problem is constructed to jointly optimize UAV-GMS association

the three dimensional (3D) deployment of UAVs

UAV denoising factors

and transmission power allocation

aiming for maximizing the system's minimum achievable rate. Giving the nonlinear characteristics of multiple constraints optimization problems

a deep deterministic policy gradient-based optimization algorithm for multiple UAVs cooperation in AirComp network (AirDDPG-UAV) is proposed to assist UAVs rapidly responding to aggregation missions in complex environments. A deterministic policy in deep reinforcement is adopted to optimize the states

behaviors

and rewards of the AirComp network

aiming to maximize the minimal achievable rate. The numerical results show that the AirDDPG-UAV algorithm can significantly improve the system's minimum achievable rate by more than 15% compared to the benchmark methods

while ensuring suitable system energy consumption and computational complexity. The AirDDPG-UAV algorithm also obtains satisfactory results in optimizing the mean MSE

which illustrates our method has excellent performance in scaling signals and thus is helpful for fast data aggregation. The experiments indicate the proposed scheme is appropriate for the distributed data aggregation with low cost and can obviously improve the efficiency and stability of data aggregation.

关键词

Keywords

references

NAZER B , GASTPAR M . Computation over multiple-access channels [J ] . IEEE Transactions on Information Theory , 2007 , 53 ( 10 ): 3498 - 3516 .

ZHU G X , XU J E , HUANG K B , et al . Over-the-air computing for wireless data aggregation in massive IoT [J ] . IEEE Wireless Communications , 2021 , 28 ( 4 ): 57 - 65 .

LI Y Q , JIANG M A , ZHANG G C , et al . Joint optimization for multi-antenna AF-relay aided over-the-air computation [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 6 ): 6744 - 6749 .

NI W L , LIU Y W , YANG Z H , et al . Over-the-air federated learning and non-orthogonal multiple access unified by reconfigurable intelligent surface [C ] // IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway : IEEE , 2021 : 1 - 6 .

MOLINARI F , AGRAWAL N , STANCZAK S , et al . Max-consensus over fading wireless channels [J ] . IEEE Transactions on Control of Network Systems , 2021 , 8 ( 2 ): 791 - 802 .

LIU W C , ZANG X , LI Y H , et al . Over-the-air computation systems: Optimization, analysis and scaling laws [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 8 ): 5488 - 5502 .

CAO X W , ZHU G X , XU J E , et al . Optimized power control for over-the-air computation in fading channels [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 11 ): 7498 - 7513 .

LI X Y , ZHU G X , GONG Y , et al . Wirelessly powered data aggregation for IoT via over-the-air function computation: Beamforming and power control [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 7 ): 3437 - 3452 .

WEN D Z , ZHU G X , HUANG K B . Reduced-dimension design of MIMO AirComp for data aggregation in clustered IoT networks [C ] // 2019 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE , 2019 : 1 - 6 .

FU M , ZHOU Y , SHI Y M , et al . UAV aided over-the-air computation [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 7 ): 4909 - 4924 .

MA X A , SUN H J , WANG Q , et al . User scheduling for federated learning through over-the-air computation [C ] // 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) . Piscataway : IEEE , 2021 : 1 - 5 .

NI W L , LIU Y W , YANG Z H , et al . Federated learning in multi-RIS-aided systems [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 12 ): 9608 - 9624 .

YANG K , JIANG T , SHI Y M , et al . Federated learning via over-the-air computation [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 3 ): 2022 - 2035 .

CHEN L , QIN X W , WEI G . A uniform-forcing transceiver design for over-the-air function computation [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 6 ): 942 - 945 .

HU Y T , CHEN M , CHEN M Z , et al . Energy minimization for federated learning with IRS-assisted over-the-air computation [C ] // ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE , 2021 : 3105 - 3109 .

WANG F , LAU V K N . Multi-level over-the-air aggregation of mobile edge computing over D2D wireless networks [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 10 ): 8337 - 8353 .

ZHU G X , HUANG K B . MIMO over-the-air computation for high-mobility multimodal sensing [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 4 ): 6089 - 6103 .

崔玉亚 , 张德干 , 张婷 , 等 . 一种面向移动边缘计算的多用户细粒度任务卸载调度方法 [J ] . 电子学报 , 2021 , 49 ( 11 ): 2202 - 2207 .

CUI Y Y , ZHANG D G , ZHANG T , et al . A multi-user fine-grained task offloading scheduling approach of mobile edge computing [J ] . Acta Electronica Sinica , 2021 , 49 ( 11 ): 2202 - 2207 . (in Chinese)

LU W D , DING Y , GAO Y A , et al . Resource and trajectory optimization for secure communications in dual unmanned aerial vehicle mobile edge computing systems [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 4 ): 2704 - 2713 .

FU M , ZHOU Y , SHI Y M , et al . UAV-assisted over-the-air computation [C ] // ICC 2021 - IEEE International Conference on Communications . Piscataway : IEEE , 2021 : 1 - 6 .

CAO X W , ZHU G X , XU J E , et al . Cooperative interference management for over-the-air computation networks [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 4 ): 2634 - 2651 .

ZENG X A , ZHANG X A , WANG F . Optimized UAV trajectory and transceiver design for over-the-air computation systems [J ] . IEEE Open Journal of the Computer Society , 2022 , 3 : 313 - 322 .

FU M , ZHOU Y , SHI Y M , et al . UAV-assisted multi-cluster over-the-air computation [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 7 ): 4668 - 4682 .

JOUNG J , FAN J C . Over-the-air computation strategy using space-time line code for data collection by multiple unmanned aerial vehicles [J ] . IEEE Access , 2021 , 9 : 105230 - 105241 .

SUN L , WAN L T , WANG X P . Learning-based resource allocation strategy for industrial IoT in UAV-enabled MEC systems [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 7 ): 5031 - 5040 .

ZHANG X C , ZHANG J A , XIONG J , et al . Energy-efficient multi-UAV-enabled multiaccess edge computing incorporating NOMA [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 6 ): 5613 - 5627 .

AL-HOURANI A , KANDEEPAN S , LARDNER S . Optimal LAP altitude for maximum coverage [J ] . IEEE Wireless Communications Letters , 2014 , 3 ( 6 ): 569 - 572 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study

Large Displacement Motion Optical Flow Estimation with Non-Rigid Dense Patch Matching

UAVS Cooperative Task Allocation Based on Multi-Objective MSQPSO Algorithm

Related Author

XIA Jing-ming

JIA Yun-jian

LI Yi-xin

WEI Ya-xing

LIANG Liang

CHEN Hao

GE Li-yue

LI Ming

Related Institution

School of Microelectronics and Communication Engineering， Chongqing University

Institute of Automation Chinese Academy of Sciences Beijing China

Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi China

Key Laboratory of Nondestructive Testing Ministry of Education Nanchang Hangkong University Nanchang Jiangxi China

Institute of Automation, Chinese Academy of Sciences

⁰