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

Abstract

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

CLC Number:

TN92

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.

Figures/Tables 19

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

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

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

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

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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)

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

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