面向通信-导航-感知一体化的应急无人机网络低能耗部署研究

doi:10.11959/j.issn.1000-436x.2022138

通信学报 ›› 2022, Vol. 43 ›› Issue (7): 1-20.doi: 10.11959/j.issn.1000-436x.2022138

• 学术论文 • 下一篇

面向通信-导航-感知一体化的应急无人机网络低能耗部署研究

王莉¹, 魏青², 徐连明², 沈渊³, 张平⁴, 费爱国¹

¹ 北京邮电大学计算机学院（国家示范性软件学院），北京 100876
² 北京邮电大学电子工程学院，北京 100876
³ 清华大学电子工程系，北京 100084
⁴ 北京邮电大学信息与通信工程学院，北京 100876

修回日期:2022-05-17 出版日期:2022-07-25 发布日期:2022-06-01
作者简介:王莉（1982- ），女，河南濮阳人，博士，北京邮电大学教授、博士生导师，主要研究方向为应急通信、能源互联网、边缘智能等
魏青（1993- ），女，湖北恩施人，北京邮电大学博士生，主要研究方向为应急通信、车载通信网络等
徐连明（1981- ），男，山东潍坊人，博士，北京邮电大学讲师，主要研究方向为通导组网、边缘网络、定位导航等
沈渊（1982- ），男，上海人，博士，清华大学教授、博士生导师，主要研究方向为定位感知、智能协同系统和生物结构解析等
张平（1959- ），男，陕西汉中人，博士，中国工程院院士，北京邮电大学教授、博士生导师，主要研究方向为先进移动通信系统等
费爱国（1955- ），男，江苏涟水人，博士，中国工程院院士，北京邮电大学教授、博士生导师，主要研究方向为指挥信息系统和数据链技术等
基金资助:
国家重点研发计划基金资助项目(2020YFC1511801);国家自然科学基金资助项目(U2066201);国家自然科学基金资助项目(62171054);北京市自然科学基金资助项目(L192030)

Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing

Li WANG¹, Qing WEI², Lianming XU², Yuan SHEN³, Ping ZHANG⁴, Aiguo FEI¹

¹ School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
² School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
³ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
⁴ School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Revised:2022-05-17 Online:2022-07-25 Published:2022-06-01
Supported by:
The National Key Research and Development Program of China(2020YFC1511801);The National Natural Science Foundation of China(U2066201);The National Natural Science Foundation of China(62171054);The Beijing Natural Science Foundation(L192030)

摘要/Abstract

摘要：

在事故灾难救援等突发公共事件中，救援人员往往面临通信传输不畅、定位导航不稳、灾情感知不准的问题，亟须部署一套应急无人机网络，对受灾地区进行通信、导航、感知服务补盲。针对无人机续航能力受限的问题，首先提出了一套网络拓扑可重构、节点角色可切换的通信-导航-感知一体化应急无人机网络低能耗部署方案，然后设计了一种基于粒子群算法的层次化匹配决策算法，联合求解无人机与通导感用户关联、多角色无人机通信资源分配以及无人机位置这3个子问题。仿真表明，所提方案能够实现通导感多目标需求与受限网络资源的灵活适配，极大降低了对无人机数量的需求与部署能耗。

关键词: 应急通信, 无人机网络部署, 通导感一体化, 无线资源分配

Abstract:

In public emergencies such as accident relief, rescue workers are faced with challenges, such as poor communication, unstable navigating, and inaccurate disaster sensing.It is necessary to deploy an emergency unmanned aerial vehicle (UAV) network to guarantee the services of communication-navigating-sensing.Aiming at alleviating the problem of limited energy of UAV, a low-energy-consumption deployment of an emergency UAV network was first proposed for integrated communication-navigating-sensing (ICNS).The proposed scheme was able to realize network topology reconstruction and role cognition on demand.Then, a particle swarm algorithm based hierarchical matching decision-making algorithm was presented to jointly optimize three sub-problems, including the associations between UAVs and users, the resource allocation for multi-role UAV communications, and the UAV position.Simulation results show that the proposed ICNS scheme can achieve flexible adaptation of the multi-objective requirements and limited network resources, and dramatically reduce the demand for the number of UAVs and the deployment energy consumption.

Key words: emergency communication, UAV network deployment, integrated communication-navigating-sensing, wireless resource allocation

中图分类号:

TN92

王莉, 魏青, 徐连明, 沈渊, 张平, 费爱国. 面向通信-导航-感知一体化的应急无人机网络低能耗部署研究[J]. 通信学报, 2022, 43(7): 1-20.

Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI. Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing[J]. Journal on Communications, 2022, 43(7): 1-20.

图/表 19

图1

表1

通导感服务需求量化的主要指标"

服务	指标	描述	数学模型
	数据速率	信道信息传输速率上限	$r = B l b (1 + SNR)$ , B表示传输带宽，SNR 表示信噪比
通信	中断概率	链路容量或信噪比小于阈值的概率	$Pr {r < r_{\min}}$ , r_min表示最小速率需求
	能量效率	单位能耗的数据速率	$EE = \frac{r}{E}$ E 表示总能耗
	均方误差	估计量与未知量的距离平方的期望	$MSE = E [(\hat{x} - x) {(\hat{x} - x)}^{T}]$ , $\hat{x}$ 表示估计值， $x$ 表示真实值
导航	几何精度因子	基本测距误差受发射端和接收端位置关系影响被放大的程度	$GDOP = \frac{\sqrt{tr (P)}}{c σ_{s}}$ , $tr (P)$ 表示估计误差的协方差矩阵的迹，c表示光速， σ_s表示到达时间的平均标准差
	克拉美罗下界	无偏估计量所能达到的最小方差	$CRLB = F^{- 1}$ , $F = E (\frac{\partial \ln p (z \| x)}{\partial x} {(\frac{\partial \ln p (z \| x)}{\partial x})}^{T})$ , $z$ 表示观测值， $x$ 表示真实值
感知	感知比例	感知任务数与总任务数的比值	$\frac{N_{S}}{N_{total}}$ ,N_S表示感知任务数，N _total表示总任务数
	覆盖范围	感知覆盖范围	$H \tan (θ)$ , H 表示无人机高度，θ表示感知角度

表1

图2

表2

主要参数"

参数	定义	参数	定义
$U^{C}$ 、 $U^{P}$ 和 $U^{S}$	通信、导航及感知服务用户集合	Q	观察时隙集合
$M^{C}$ 、 $M^{P}$ 和 $M^{S}$	通信、导航及感知无人机集合	$M$	无人机集合
$P$ 、 $B$ 和 $S$	无人机功率分配、频谱分配及位置矩阵	$U$	用户集合
$G_{m}^{C}$ 、 $G_{m}^{P}$ 和 $G_{m}^{S}$	无人机m提供通信、导航或感知服务时的能耗	$A$	无人机与通导感用户关联
$α_{m, u}^{C}$ 、 $α_{m, u}^{P}$ 和 $α_{m, u}^{S}$	无人机m是否为用户u提供通信、导航或感知服务	E^P	无人机推动功耗
$γ_{\min}^{C}$ 、 $γ_{\min}^{P}$ 和 $γ_{\min}^{S}$	通信、导航及感知服务的SINR需求	$E_{m}^{H}$	无人机m的悬停功耗
C ₀、C₁和C₂	通信、导航及感知服务数据大小	$C_{k}$	无人机定位子组k
$γ_{m, u}^{C}$ 和 $R_{m, u}^{C}$	无人机m与通信用户u间SINR和数据速率	P_max	无人机最大发射功率
$γ_{u, m}^{P,U}$ 和 $R_{m, u}^{P,U}$	无人机m与导航用户u间SINR及数据速率	B_m	无人机m的频谱带宽
$γ_{m, u}^{S}$ 和 $R_{m, u}^{S}$	无人机m与感知用户u间SINR和数据速率	P_total	无人机总发射功率
$γ_{k_{1}, k_{0}}^{P,V}$ 和 $R_{k_{1}, k_{0}}^{P,V}$	副站无人机k₁与主站无人机k₀的SINR及数据速率	$h_{m, u}^{AG}$	无人机m与用户u间信道增益
$γ_{k_{2}, k_{0}}^{P,V}$ 和 $R_{k_{2}, k_{0}}^{P,V}$	副站无人机k₂与主站无人机k₀的SINR及数据速率	$h_{m, n}^{AA}$	无人机m与无人机n间信道增益
$I_{u, k}^{P}$	无人机子组 $C_{k}$ 是否为导航用户u提供服务	N_max	无人机最大服务次数
$s_{m}$	无人机m位置向量	$q_{u}$	用户u位置向量
$r_{m}^{M}$	无人机m状态向量	$r_{u}^{U}$	用户u状态向量
B_total	无人机总频谱带宽	σ²	噪声功率大小
d_min	无人机间安全距离	L_min	感知半径需求
d_max	无人机最大移动距离	J_max	最大定位误差

表2

图3

表3

无人机部署和通信资源分配优化阶段的子问题优化变量和算法"

子问题	优化变量	算法
无人机与通导感用户关联优化	$A (q)$	层次化匹配算法
多角色无人机通信资源优化	$P (q)$ 和 $B (q)$	凸优化
无人机部署优化	$S (q)$	粒子群算法

表3

图4

表4

仿真中的主要参数设置"

参数	取值	参数	取值	参数	取值
$γ_{\min}^{C}$	3 dB	C₀	1 Mbit	p_U	0.2 W
$γ_{\min}^{P}$	4 dB	C₁	1 kbit	θ₀	45°
$γ_{\min}^{S}$	2 dB	C₂	2 Mbit	α	2
σ²	- 60 dBm	P_max	0.2 W	δ₀	50 m
J_max	10 m	P_total	0.3 W	d_min	5m
E_max	10⁶J	.d_max .	25 m	ε₀	5
V	13 m/s	L_min	$\frac{\sqrt{2}}{2} δ_{0}$	d₀	1m

表4

图5

图6

图7

图8

图9

表5

图10

表6

表7

图11

图12

参考文献 34

[1]	MASE K . How to deliver your message from/to a disaster area[J]. IEEE Communications Magazine, 2011,49(1): 52-57.
[2]	MUKHERJEE B , HABIB M F , DIKBIYIK F . Network adaptability from disaster disruptions and cascading failures[J]. IEEE Communications Magazine, 2014,52(5): 230-238.
[3]	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.
[4]	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.
[5]	ARAFAT M Y , MOH S . Localization and clustering based on swarm intelligence in UAV networks for emergency communications[J]. IEEE Internet of Things Journal, 2019,6(5): 8958-8976.
[6]	ATIF M , AHMAD R , AHMAD W ,et al. UAV-assisted wireless localization for search and rescue[J]. IEEE Systems Journal, 2021,15(3): 3261-3272.
[7]	HU J Z , ZHANG H L , SONG L Y . Reinforcement learning for decentralized trajectory design in cellular UAV networks with sense-and-send protocol[J]. IEEE Internet of Things Journal, 2019,6(4): 6177-6189.
[8]	MENG K T , LI D S , HE X F ,et al. Space pruning based time minimization in delay constrained multi-task UAV-based sensing[J]. IEEE Transactions on Vehicular Technology, 2021,70(3): 2836-2849.
[9]	ZHANG S H , ZHANG H L , HAN Z ,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.
[10]	CHEN X , FENG Z Y , WEI Z Q ,et al. Performance of joint sensing-communication cooperative sensing UAV network[J]. IEEE Transactions on Vehicular Technology, 2020,69(12): 15545-15556.
[11]	ZHAO Y , LI Z , CHENG N ,et al. Joint UAV position and power optimization for accurate regional localization in space-air integrated localization network[J]. IEEE Internet of Things Journal, 2021,8(6): 4841-4854.
[12]	SHEN Y , WYMEERSCH H , WIN M Z . Fundamental limits of wideband localization—part II:cooperative networks[J]. IEEE Transactions on Information Theory, 2010,56(10): 4981-5000.
[13]	WAN P W , HUANG Q D , LU G Y ,et al. Passive localization of signal source based on UAVs in complex environment[J]. China Communications, 2020,17(2): 107-116.
[14]	LONG T , OZGER M , CETINKAYA O ,et al. Energy neutral Internet of drones[J]. IEEE Communications Magazine, 2018,56(1): 22-28.
[15]	BAI B , WANG L , HAN Z ,et al. Caching based socially-aware D2D communications in wireless content delivery networks:a hypergraph framework[J]. IEEE Wireless Communications, 2016,23(4): 74-81.
[16]	DAI Z C , WANG G , JIN X P ,et al. Nearly optimal sensor selection for TDOA-based source localization in wireless sensor networks[J]. IEEE Transactions on Vehicular Technology, 2020,69(10): 12031-12042.
[17]	TORRIERI D J . Statistical theory of passive location systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 1984,20(2): 183-198.
[18]	ZHANG S H , ZHANG H L , DI B Y ,et al. Joint trajectory and power optimization for UAV sensing over cellular networks[J]. IEEE Communications Letters, 2018,22(11): 2382-2385.
[19]	SUN S , ZHANG G P , MEI H B ,et al. Optimizing multi-UAV deployment in 3-D space to minimize task completion time in UAV-enabled mobile edge computing systems[J]. IEEE Communications Letters, 2021,25(2): 579-583.
[20]	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.
[21]	SOHAIL M F , LEOW C Y , WON S . Energy-efficient non-orthogonal multiple access for UAV communication system[J]. IEEE Transactions on Vehicular Technology, 2019,68(11): 10834-10845.
[22]	WANG L , GUAN M L , AI Y T ,et al. Beamforming-aided NOMA expedites collaborative multiuser computational offloading[J]. IEEE Transactions on Vehicular Technology, 2018,67(10): 10027-10032.
[23]	夏伟 . 多站无源时差定位系统布站方法研究[D]. 西安:西安电子科技大学, 2019.
	XIA W . Stations distribution study of passive time difference localization system using multiple sensors[D]. Xi’an:Xidian University, 2019.
[24]	3GPP. Technical specification group core network and terminals;control plane location services (LCS) procedures in the evolved packet system (EPS),Release 16[S]. 3GPP TS 24.171, 2020.
[25]	BOYD S , VANDENBERGHE L . Convex optimization[M]. Cambridge: Cambridge University Press, 2004.
[26]	KUHN H W . The Hungarian method for the assignment problem[J]. Naval Research Logistics Quarterly, 1955,2(1/2): 83-97.
[27]	VALLE Y D , VENAYAGAMOORTHY G K , MOHAGHEGHI S ,et al. Particle swarm optimization:basic concepts,variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation, 2008,12(2): 171-195.
[28]	LIANG B , HAAS Z J . Predictive distance-based mobility management for multidimensional PCS networks[J]. IEEE/ACM Transactions on Networking, 2003,11(5): 718-732.
[29]	AL-HOURANI A , KANDEEPAN S , JAMALIPOUR A . Modeling air-to-ground path loss for low altitude platforms in urban environments[C]// Proceedings of 2014 IEEE Global Communications Conference. Piscataway:IEEE Press, 2014: 2898-2904.
[30]	AL-HOURANI A , KANDEEPAN S , LARDNER S . Optimal LAP altitude for maximum coverage[J]. IEEE Wireless Communications Letters, 2014,3(6): 569-572.
[31]	BOR-YALINIZ R I , EL-KEYI A , YANIKOMEROGLU H . Efficient 3-D placement of an aerial base station in next generation cellular networks[C]// Proceedings of 2016 IEEE International Conference on Communications. Piscataway:IEEE Press, 2016: 1-5.
[32]	3GPP. Base station (BS) radio transmission and reception,Release 17[S]. 3GPP TS 38.104, 2021.
[33]	3GPP. Physical channels and modulation,Release 17[S]. 3GPP TS 38.211, 2022.
[34]	王超 . 无人机基站部署与位置更新研究[D]. 西安:西安电子科技大学, 2019.
	WANG C . Deployment and location updating of UAV base stations[D]. Xi’an:Xidian University, 2019.

方案	用户速度/(m·s^-1)	能耗均值/J	能耗方差/J²
	0.4	177.38	237.43
非通导感一体化	0.8	177.42	229.44
	1.2	178.07	204.60
	0.4	162.45	54.97
通导感一体化	0.8	162.47	52.90
	1.2	163.25	48.24

方案名称	是否通导感一体化	部署方法
ICNS-LEC	是	所提LEC算法
ICNS-Greedy	是	贪婪算法
nonICNS-LEC	否	所提LEC算法

频率	带宽	额外路损(η_LoS,η_NLoS)
700 MHz	20 MHz	(0, 18)
2 GHz	30 MHz	(0.1, 21)

面向通信-导航-感知一体化的应急无人机网络低能耗部署研究

Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing

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