基于多密度流聚类的UAV-NOMA用户分簇与功率分配算法

针对无人机(Unmanned Aerial Vehicle,UAV)辅助非正交多址(Non-Orthogonal Multiple Access,NOMA)下行通信系统,提出了最大化和速率的用户动态分簇与功率分配方案.考虑用户服务质量与UAV位置约束,建立了和速率最大化的优化问题.由于目标函数的非凸性,将原问题解耦为三个子问题,分别优化UAV位置部署与用户连接、用户动态分簇、功率分配以提高系统性能.首先,基于K-means算法设计了UAV位置部署与用户连接方案,以减小路损为目的确定UAV最佳部署位置,同时选择其服务的最优用户群;其次,改进多密度流聚类(Multi-Density Stream Clustering, MDSC)算法,提出了单UAV下用户静态与动态分簇方案,静态分簇方案可自适应平衡簇数与簇用户数,并获得较大的簇内用户信道增益差异,动态分簇方案则针对用户移动属性,制定了即时更新策略;最后,使用分式规划(Fractional Programming,FP)二次变换的方法,引入辅助变量将原非凸问题变换为凸问题,交替优化辅助变量与功率分配因子,获得原非凸问题的次优解.仿真结果表明,与其他算法相比,本文分簇方案能获得更大的簇内信道差异与更小的簇内用户数标准差,同时用户系统性能也获得了显著提升.

Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning

Aiming at the suppression of enemy air defense(SEAD)task under the complex and complicated combat sce-nario,the spatiotemporal cooperative path planning methods are studied in this paper.The major research contents include opti-mal path points generation,path smoothing and cooperative rendezvous.In the path points generation part,the path points availability testing algorithm and the path segments availability testing algorithm are designed,on this foundation,the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path.In the path smoothing part,taking ter-minal attack angle constraint and maneuverability constraint into consideration,the Dubins curve is introduced to smooth the path segments.In cooperative rendezvous part,we take esti-mated time of arrival requirement constraint and flight speed range constraint into consideration,the speed control strategy and flight path control strategy are introduced,further,the decoupling scheme of the circling maneuver and detouring maneuver is designed,in this case,the maneuver ways,maneu-ver point,maneuver times,maneuver path and flight speed are determined.Finally,the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles(UAVs)is effectively real-ized,in this way,the combat situation suppression against the enemy can be realized in SEAD scenarios.

Sensing-Communication-Computing-Control Closed Loop for NOMA-UAV Systems

In the areas without terrestrial communication infrastructures,unmanned aerial vehicles(UAVs)can be utilized to serve field robots for mission-critical tasks.For this purpose,UAVs can be equipped with sensing,communication,and computing modules to support various requirements of robots.In the task process,different modules assist the robots to perform tasks in a closed-loop way,which is referred to as a sensing-communication-computing-control(SC3)loop.In this work,we investigate a UAV-aided system containing multiple SC^(3)loops,which leverages non-orthogonal multiple access(NOMA)for efficient resource sharing.We describe and compare three different modelling levels for the SC^(3)loop.Based on the entropy SC^(3)loop model,a sum linear quadratic regulator(LQR)control cost minimization problem is formulated by optimizing the communication power.Further for the assure-to-be-stable case,we show that the original problem can be approximated by a modified user fairness problem,and accordingly gain more insights into the optimal solutions.Simulation results demonstrate the performance gain of using NOMA in such task-oriented systems,as well as the superiority of our proposed closed-loop-oriented design.

UAV协助下非正交多址接入使能的数据采集系统中能效优化机制

无人机(UAV)协助下非正交多址接入(NOMA)使能的数据采集系统,考虑了地空概率信道模型和服务质量约束,并联合优化UAV三维布局设计和传感器功率分配最大化所有传感器的总能效。针对原混合整数非凸规划问题,提出了一种基于凸优化理论、深度学习理论和哈里斯鹰优化(HHO)算法的能效优化机制。在任意给定的UAV三维布局下,首先将功率分配子问题等价转化为凸优化问题;其次基于最优的功率分配方案,采用深度神经网络(DNN)构建从传感器位置到UAV三维布局的映射,并利用HHO算法离线训练最佳映射对应的模型参数。训练后的机制仅需执行少量代数运算并求解单个凸优化问题。仿真实验结果表明,在传感器数为12的情况下,相较于基于粒子群算法的遍历搜索机制,所提机制在仅损失约4.73%的总能效的情况下将运算时间降低了5个数量级。

Outage Performance of Non-Orthogonal Multiple Access Based Unmanned Aerial Vehicles Satellite Networks

With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to integrate UAVs into the satellite network, where multiple satellites cooperatively serve the UAVs and mobile terminal using the Ku-band and above. Taking into account the rain fading and the fading correlation, the outage performance is first analytically obtained for fixed power allocation and then efficiently calculated by the proposed power allocation algorithm to guarantee the user fairness. Simulation results verify the outage performance analysis and show the performance improvement of the proposed power allocation scheme.

Optimal UAV deployment in downlink non-orthogonal multiple access system:a two-user case

This paper investigates a unmanned aerial vehicle(UAV)deployment problem in a non-orthogonal multiple access(NOMA)system,where the UAV is deployed as an aerial mobile base station to transmit data to two ground users.An optimization problem is formulated by deploying the UAV for maximizing the sum rate of the two users.In order to solve the optimization problem,the feasible solution region is first reduced to a line segment between two users.Then,the optimization problem is simplified to a univariate problem,which can be solved by derivation under a certain situation,and the corresponding analytical solution is also provided.Moreover,a generalized algorithm,which considers 2 situations,is proposed to further determine the optimal UAV’s location.Specifically,four cases are discussed in the first situation.Extensive simulations are depicted to demonstrate effectiveness of the proposed algorithm and its superiority over the benchmarks in maximizing the two users’sum rate.

基于智能反射面辅助的UAV转发应急通信网络性能分析

在蜂窝基站无法提供通信覆盖区域时,可利用无人机作为转发节点构建无人机应急通信系统。为了提高应急通信系统的频谱效率和降低通信的中断概率,提出基于智能反射面(Intelligent Reflecting Surface,IRS)辅助的无人机转发的应急通信架构。该架构中蜂窝基站采用非正交多址接入(Non Orthogonal Multiple Access,NOMA)技术,并在无人机机身上安装IRS。IRS将来自蜂窝基站传输的信号反射至地面用户,克服蜂窝基站与用户间的非视距链路环境,进而降低通信的中断概率。同时,推导了中断概率的闭合表达式,并给出了计算遍历频谱效率上限公式。仿真结果验证了理论分析的正确性及所提出通信架构的优势性。

无人机高能效立体覆盖中轨迹与资源优化

“泛在覆盖”将成为6G的主流网络形式,完成在高山、丘陵、沙漠等网络盲区的通信部署,实现全域无线覆盖,但在远区大规模部署地面基站较为困难。为此,该文将无人机(UAV)通信与非正交多址接入(NOMA)相结合,提出一种高能效立体覆盖方案最大化网络吞吐量能效。首先,建立系统模型,基于K-Means算法与Gale-Shapley算法提出用户配对方案。其次,在用户配对完成后,将初始问题拆分为两个优化子问题并分别转化为凸。最后,利用块坐标上升法交替优化无人机轨迹和发射功率最大化能量效率。仿真结果表明,相较于其它基准方案,该文方案可以显著提高大规模无线覆盖下空地网络的吞吐量能效。

给定时间有向通信网络多无人机最优集结控制

为解决给定时间的多无人机(unmanned aerial vehicles,UAVs)最优集结问题,通过分布式优化的方法研究能够使多无人机在权重不平衡有向通信网络下按照给定时间范围内完成最优集结的控制算法。每架无人机都有其相应的局部目标函数,全局目标函数就是每架无人机所具有的局部目标函数之和,算法的目的就是通过分布式控制的方式,找到满足全局目标函数的最优集结点,采用时域映射的思想,将原本的给定时间下的集结问题转变为无限时域下的集结问题,并通过拉普拉斯零特征值下的左特征向量克服权重不均衡有向网络的不平衡性。结合凸分析理论和李雅普诺夫稳定性理论,验证了算法能够收敛到最优的解。仿真结果表明:不同出发点的无人机,在算法的控制下,均可以在给定时间内到达最优的集结点。

基于无人机多光谱影像的云南松林蓄积量估测模型

【目的】无人机多光谱遥感影像较可见光影像具有更丰富的光谱信息,在森林蓄积量估测中具有较大潜力。以无人机载多光谱遥感影像为主要数据源,探索森林蓄积量的遥感估测模型,以克服传统地面调查工作量大、耗时长、成本高等弊端。【方法】以滇中地区典型天然云南松Pinusyunnanensis纯林为研究对象,利用无人机多光谱影像提取单波段反射率、各类植被指数、纹理特征等,计算各特征变量的标准地均值;筛选与云南松林蓄积量相关性显著的特征变量,采用多元线性、随机森林、支持向量机建立云南松林蓄积量估测模型,以决定系数(R^(2))、平均绝对误差(E_(MA))、均方根误差(E_(RMS))、平均相对误差(EMR)评价模型精度。【结果】①3种模型中,随机森林的精度最高(R^(2)=0.89,E_(MA)=4.69 m^(3)·hm^(-2),E_(RMS)=5.45 m^(3)·hm^(-2),EMR=14.5%),其次为支持向量机(R^(2)=0.74,E_(MA)=5.27 m^(3)·hm^(-2),E_(RMS)=8.31 m^(3)·hm^(-2),EMR=13.1%),最低为多元线性回归模型(R^(2)=0.35,E_(MA)=10.12 m^(3)·hm^(-2),E_(RMS)=12.85 m^(3)·hm^(-2),EMR=28.1%);3种模型在测试集上的估测精度均有所降低,随机森林的模型表现最好,支持向量机次之,多元线性最差。②3种模型在云南松林蓄积量估测中均存在一定的低值高估和高值低估现象。③基于无人机多光谱影像估测云南松林蓄积量,纹理特征仍是不可忽视的重要因子。【结论】基于无人机多光谱影像,在不进行单木分割的情景下,提取标准地的单波段反射率、植被指数、纹理特征均值,筛选适用于蓄积量估算的变量构建估测模型。通过对3种模型进行精度评价,随机森林为云南松林蓄积量估测的最佳模型。

无人机边缘计算:架构、多址接入与计算卸载

无人机(unmanned aerial vehicle,UAV)边缘计算技术将UAV平台与移动边缘计算技术相结合,充分利用UAV的灵活性和机动性,为用户设备提供及时有效的计算服务。从UAV边缘计算的网络架构入手,提出基于网络功能虚拟化和软件定义网络的技术架构。针对UAV边缘计算的关键技术,总结对比UAV边缘计算中不同的多址接入方案,并从不同的优化目标出发,对基于经典非凸优化、博弈论以及人工智能方法的计算卸载策略进行总结和分析。最后,探讨和展望未来的研究方向。

Nakagami-m信道下无人机辅助的速率分拆多址接入协作通信系统性能研究

针对多用户通信资源短缺、分配不均衡的问题,该文研究了基于速率分拆多址接入技术(RSMA)的无人机(UAV)辅助多用户下行通信网络。在复杂的实际通信环境中,频率复用所引起的无用信号的干扰不可避免,考虑无人机与各用户节点信息传输受共道干扰影响,在Nakagami-m衰落信道下推导了该无人机协作通信系统的中断概率和信道容量的精确闭式表达式,证明共道干扰的存在使得高信噪比(SNR)区域系统的分集阶数为0。结果表明,在相同的空间模型下,采用RSMA通信方案的系统性能优于非正交多址(NOMA)方案;无人机飞行速度增大时,地-空通信建立视距链路的概率降低,系统中断性能下降。因此,在满足用户实际通信的需求时需要综合考虑无人机飞行速度、多址接入方式、系统性能以及通信连通性,以实现对无人机通信系统整体的有效权衡。

无人机通信中的资源分配及部署位置联合优化

为提升基于正交频分多址接入模式无人机辅助无线通信系统的网络性能,首先,以提升用户的公平性为系统方案设计指标,将包括子信道分配、调制模式选择、功率分配等通信资源和无人机位置联合建模为一个混合整数非线性优化问题;进一步,利用迭代优化的方式解决变量耦合性及非凸性等问题,将最大-最小问题转换为两个子问题:子信道分配和调制方式选择联合优化、无人机位置和子信道功率联合优化;然后,通过适当变换将子信道分配和调制方式选择联合优化建模为0-1线性优化问题进行求解,而无人机位置和子信道功率联合优化建模为凸优化问题求解;最后,进行实验仿真验证.研究结果表明,所提联合优化算法相比基本方案可有效提升网络用户的公平性.

基于无人机多光谱图像和集成学习的橡胶树白粉病检测

橡胶树是我国重要的热带经济作物,其生长过程中易受白粉病的侵染,准确、及时地监测橡胶树白粉病是防止其大规模蔓延的关键。近年来,无人机遥感技术在农林领域得到了广泛应用,本研究评估了采用低空遥感技术大规模检测橡胶树白粉病的可行性,并致力于提高检测的准确性。基于大疆精灵4多光谱无人机获取橡胶树冠层多光谱图像,计算植被指数(VI)和纹理特征(TF),然后结合皮尔逊相关系数(PCCs)和Boruta-SHAP算法进行相关性分析和特征重要性分析,去除冗余特征,Blue-MEA、WI、DVIRE、PPR和GI被选为最佳特征组合,最后基于K近邻(KNN)、朴素贝叶斯(Bayes)、支持向量机(SVM)、随机森林(RF)、极端梯度提升(XGB)和Stacking集成算法构建橡胶树白粉病监测模型。结果表明:经特征筛选后,Stacking集成模型的准确率(OA)和Kappa(KC)值分别达到96.39%和92.78%,相比于5个单一基础模型KNN、Bayes、SVM、RF、XGB分类的效果,集成学习模型的准确率分别提高了3.15%、5.52%、1.80%、3.04%、1.14%,Kappa值提高了6.32%、11.05%、3.61%、6.09%、2.27%;在绘制橡胶树白粉病空间分布图时,使用17×17窗口大小的像素聚合策略分类准确率最高(OA=96.22%)。

状态约束下的多飞行器有限时间姿态一致性控制

执行机构的物理限制等因素的影响,多飞行器姿态一致性控制面临姿态和角速度约束的挑战,加大了设计有效姿态一致性控制器的技术难度。针对此问题,提出了一种状态约束(含姿态和角速度约束)下的多飞行器有限时间姿态一致性控制器。利用非光滑理论和代数图论的知识设计了一类有限时间姿态一致性控制策略,并结合障碍Lyapunov函数技术构建了一类新颖的姿态一致性跟踪控制器。理论分析表明,提出的控制器能够在满足姿态和角速度约束条件下实现所有飞行器的姿态在有限时间内跟踪到期望姿态值。该方法具备结构简单、自定义约束适应性强和快速收敛等优点。仿真结果表明,所提的方法能够在满足姿态和角速度约束的条件下实现多飞行器对期望姿态的有限时间一致性跟踪。

卫星MIMO通信系统抗移动无人机集群干扰的在线BSS算法

移动无人机(UAV)集群可以对卫星多输入多输出(MIMO)通信系统下行链路近距离实施压制式恶意干扰,导致接收信号的信干比(SIR)很低,严重影响通信质量。由于移动无人机信号的传输具有快衰落特性,因此此类扰信混合不仅具有时变性,而且存在突变性。采用盲源分离(BBS)算法实现扰信分离,提出一种部分回溯自适应变步长动量项等变自适应盲分离(PR-V-M-EASI)算法。算法以自适应变步长动量项EASI算法为基础,将串音误差作为分离性能指标自适应调整步长和动量项因子,加快算法的迭代速度,以适应混合矩阵的时变性。此外,算法还通过部分回溯分离的方式提升混合矩阵突变阶段的分离性能,降低分离算法的复杂度。仿真结果表明,PR-V-M-EASI算法能够在不影响收敛速度的前提下改善混合矩阵突变导致的分离精度下降,可有效对抗移动无人机集群施放的强恶意干扰。

NOMA-Based UAV-Aided Networks for Emergency Communications

High spectrum efficiency(SE)requirement and massive connections are the main challenges for the fifth generation(5G)and beyond 5G(B5G)wireless networks,especially for the case when Internet of Things(IoT)devices are located in a disaster area.Non-orthogonal multiple access(NOMA)-based unmanned aerial vehicle(UAV)-aided network is emerging as a promising technique to overcome the above challenges.In this paper,an emergency communications framework of NOMA-based UAV-aided networks is established,where the disasters scenarios can be divided into three broad categories that have named emergency areas,wide areas and dense areas.First,a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas.Then,a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas.Furthermore,a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas.Simulation results are provided to validate the effectiveness of the above three schemes.Finally,potential research directions and challenges are also highlighted and discussed.

NOMA-Based UAV Communications for Maritime Coverage Enhancement

The lack of communication infrastructure in the ocean inevitably leads to coverage blind zones.In addition to high-throughput marine satellites,unmanned aerial vehicles(UAVs)can be used to provide coverage for these blind zones along with onshore base stations.In this paper,we consider the use of UAV for maritime coverage enhancement.Particularly,to serve more ships on the vast oceanic area with limited spectrum resources,we employ non-orthogonal multiple access(NOMA).A joint power and transmission duration allocation problem is formulated to maximize the minimum ship throughput,with the constraints on onboard communication energy.Different from previous works,we only assume the slowly time-varying large-scale channel state information(CSI)to reduce the system cost,as the large-scale CSI is locationdependent and can be obtained according to a priori radio map.To solve the non-convex problem,we decompose it into two subproblems and solve them in an iterative way.Simulation results show the effectiveness of the proposed solution.

Improved IMM algorithm based on support vector regression for UAV tracking

With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.

Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments

Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.