Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems

This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV. Since the problem is non-convex, an alternating optimization (AO) algorithm is proposed to solve it. Specifically, the TPC matrix and AN covariance matrix are derived by the Lagrange dual method. The alternating direction method of multipliers (ADMM), majorization-minimization (MM), and Riemannian manifold gradient (RCG) algorithms are presented, respectively, to solve the IRS phase shift matrix, and then the performance of the three algorithms is compared. Based on the proportional integral (PI) control theory, a secrecy rate gradient (SRG) algorithm is proposed to iteratively search for the UAV position by following the direction of the secrecy rate gradient. The theoretic analysis and simulation results show that our proposed AO algorithm has a good convergence performance and can increase the SR by 40.5% compared with the method without IRS assistance.     

COLREGS-based collision avoidance algorithm for unmanned surface vehicles using modified artificial potential fields

In this paper, to improve the safety of navigation in dynamic environments, a new collision avoidance method is presented for Unmanned Surface Vehicles (USVs). It is mainly focused on dynamic obstacles and safety requirements. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is introduced and the original artificial potential field (APF) algorithm is modified to ensure safe collision avoidance of USVs. The new attractive and repulsive potential field functions are designed and a new process of dynamic collision avoidance is constructed to solve the issue. At last, theoretical simulations and field tests are launched to validate our method. The simulation results show that the proposed method can perform safe navigation well. Through our USV platform, the effectiveness of the improved APF method for collision avoidance is also verified. Meanwhile, it is fast, effective, and deterministic.     

多无人机三维群集算法的适用性研究与改进

针对多无人机群三维空间运动的复杂群集控制问题,提出了基于生物群集行为、依据Reynolds规则描述的三维群集控制算法。已有的研究大多将无人机群集运动简化为二维平面运动,但这不符合实际控制需求。为此,将群集控制算法和人工势场算法推广到三维无人机群集控制中,建立了三维无人机群空间运动模型,通过多种不同条件下的仿真,研究了两种算法在三维群集控制中的有效性。结果显示两种算法用于三维群集控制均具有一定效果,但相对二维所需要的条件更为苛刻。同时,注意到智能算法具有更好的群体聚集效果,而人工势场算法则避碰效果更迅速明显。据此,对人工势场算法和智能算法进行了改进,通过在距离大于平衡点时采用智能算法聚集,在距离小于平衡点时采用人工势场算法避碰,得到能同时获得更好的聚集、避碰效果的新的群集控制算法。     

自适应功率分配可见光MIMO信道容量研究

可见光通信（VLC）系统通常采用多阵列光源布局方式来兼顾照明与通信双重功能,因此需要使用多输入多输出（MIMO）技术进行多天线协同传输来实现高速率通信。然而传统MIMO系统中采用平均功率分配来实现空间复用,无法充分体现MIMO多天线协调传输的优势。根据每组收发天线信道状态的差异,设计了一种低计算复杂度的快速迭代注水算法,可实现依据信道特征自适应的分配信息,从而提高系统的信道容量。仿真结果表明:在相同信噪比情况下注水算法自适应功率分配系统比等功率分配系统的信道容量提高了1.25 bit/Hz左右。     

无人机匹配地形飞行的无线紫外光引导方法

使用紫外光引导无人机飞行,具有抗干扰能力强、全天候非直视通信、保密通信能力强和适用于特殊场合等优点。因此,提供一种无人机匹配地形飞行的无线紫外光引导方法,以辅助无人机匹配不同地形飞行。提出一种基于紫外光的光功率控制方法,调整紫外信标发送端的角度和发射功率,使得到达无人机飞行平面的紫外光的功率相等,从而保障无人机安全飞行。对功率控制方法进行了计算机仿真。实验结果表明,紫外光LED发送张角不超过80°时,功率容易控制,信标集成易于实现,且受地形、高度等因素影响较小。     

低复杂度的MIMO声呐协方差矩阵重构方法*

多输入多输出声纳在对目标进行测向时会产生复杂的运算量,从而降低算法的测向效率。针对这一问题,提出了一种基于降维变换方法的低复杂度协方差矩阵重构方法。该方法能够抑制噪声,提高目标侧向性能。首先利用降维变换方法对接收信号进行波束形成,获得低维度的协方差矩阵,再对矩阵进行Toeplitz处理,抑制矩阵的相干性。所得到的新的协方差矩阵,通过特征分解获得噪声子空间和信号子空间,利用MUSIC方法进行测向。为了进一步降低运算复杂度,利用阵型所满足的旋转不变性,可以采用ESPRIT算法对目标进行波达方向估计。理论分析和实验结果表明,该方法有效降低了运算复杂度,提高了算法的测向性能。在有限快拍数的情况下,与传统测向方法相比,具有运算速度快,目标分辨力强的特点。     

URLLC-oriented secure communication for UAV relay-assisted network

In this paper, we investigate the unmanned aerial vehicle (UAV) relay-assisted secure short packet communication. The UAV acts as a decode-and-forward relay to transmit control signals from the source to the actuators in the presence of a ground eavesdropper (EV) whose imperfect channel state information is available at the UAV. Specially, non-orthogonal multiple access is adopted in our work to achieve more connections and improve the fairness of communication and the short packets are employed for data transmission to reduce the latency. Explicitly, we maximize the minimum average secrecy throughput among all actuators by jointly optimizing the UAV trajectory, transmit power and blocklength allocation, which generates a challenging optimization problem. Therefore, we propose an iterative algorithm based on block coordinate descent method and successive convex approximation technique to handle the non-convex problem. Numerical results show that the proposed scheme has better performance compared to the benchmark schemes.     

Secured data transmissions in corporeal unmanned device to device using machine learning algorithm

Cyber–physical systems (CPS) for device-to-device (D2D) communications are gaining prominence in today’s sophisticated data transmission infrastructures. This research intends to develop a new model for UAV transmissions across distinct network nodes, which is necessary since an automatic monitoring system is required to enhance the current D2D application infrastructure. The real time significance of proposed UAV for D2D communications can be observed during data transmission state where individual data will have huge impact on maximizing the D2D security. Additionally, through the use of simulation, an exploratory persistence tool is offered for CPS networks with fully characterized energy issues. This UAV CPS paradigm is based on mobility nodes, which host concurrent systems and control algorithms. In sixth-generation networks, when there are no barriers and the collision rate is low and the connectivity is fast, the method is also feasible. Unmanned aerial vehicles (UAVs) can now cover great distances, even while encountering hazardous obstacles. When compared to the preexisting models, the simulated values for autonomous, collision, and parametric reliability are much better by an average of 87%. The proposed model, however, is shown to be highly independent and exhibits stable perceptual behaviour. The proposed UAV approach is optimal for real-time applications due to its potential for more secure operation via a variety of different communication modules.     

An overview of simultaneous wireless information and power transfer in massive MIMO networks: A resource allocation perspective

Providing a stable and perpetual source of energy to charge battery-powered wireless communication devices is viewed as a major challenge in wireless communication systems. This challenge leads to the trending research area where radio frequency signals are being exploited for energy harvesting purposes. The technique for achieving this is known as simultaneous wireless information and power transfer (SWIPT). In recent studies on SWIPT, the massive Multiple-Input-Multiple-Output (MIMO) aided energy harvesting has attracted considerable attention from the research community. This can be attributed to the high energy delivery rate of massive MIMO antenna systems due to their capacity to focus transmitted signals in the direction of the intended receivers. However, SWIPT in massive MIMO networks requires an optimal design to achieve a proper balance between different conflicting network objectives. In this article, we aim to discuss various contributions to SWIPT in massive MIMO networks in order to address critical design issues. In particular, we focus on the widely adopted approach to resolving SWIPT-related issues in massive MIMO networks, that is, the resource allocation design. We also extend our discussion to studies dedicated to solving critical design challenges. In this regard, we take into consideration the energy efficiency and security aspect of the system design. Finally, we identify potential areas that can be explored for future research work.     

基于负氢离子源的全三维PIC/MCC模拟算法研究

在分析负氢离子源中等离子体物理机理基础下, 研究并优化粒子模拟算法, 设计高效的粒子存储方法. 研究并运用粒子碰撞蒙特卡罗方法, 考虑等离子体势以及带电粒子间库仑碰撞, 研制了全三维粒子模拟/蒙特卡罗算法(PIC/MCC). 采用磁荷模型, 运用时域有限差分方法计算多峰磁场, 并结合国外负氢离子源JT-60U, 考虑负氢离子源中主要反应, 对全三维PIC/MCC模拟算法模拟验证.     

集成SIFT算法与检测模型优化的UAV影像匹配方法

低空无人机(UAV)测量凭借着低成本、高效率、高精度的数据采集模式,可快速获取高空间分辨率的影像数据,已经成为遥感领域的一种重要技术手段.其中,影像匹配技术是UAV影像数据处理的重要步骤,图像间的匹配直接影响后期三维场景的精度及视觉效果.针对高原山地的高差起伏变化大地形复杂,植被覆被率高及地物分布不规则等问题存在,致使...     

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.     

Deep learning-driven MIMO: Data encoding and processing mechanism

Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems.     

A PARAFAC estimator for MIMO radar under direction-dependent mutual coupling

Among the various research branches in multiple-input multiple-output (MIMO) radar, Direction finding is an interesting topic and has attracted extensive concerns. However, many previous strategies are only effective to deal with scenarios without model error, such as well-calibrated sensor array, which is unrealistic in practice. This paper revisits the direction finding issue in a bistatic MIMO radar, in which the direction-dependent mutual coupling (MC) effect in both the transmitting array and the receiving array are considered. An estimator based on parallel factor (PARAFAC) decomposition is introduced. Benefit from the fact that the multidimensional structure of the tensor measurement can be explored, the proposed PARAFAC estimator can offer closed-form solution to direction finding without additional pairing calculation, so it is much more accurate and efficient than the state-of-the-art spectrum search method and the rotational invariance algorithm. The improved PARAFAC approach is mathematically analyzed in detail, Several computer trials are carried out to show the theoretical advantages.     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

双基地多输入多输出虚拟阵列的稳健低旁瓣波束优化技术

双基地多输入多输出(MIMO)系统需要对目标的波达角和波离角进行二维估计,针对高分辨算法中阵列协方差矩阵处理运算量大以及估计性能对阵列流形失配敏感的缺陷,提出了一种基于二阶锥规划的抗阵列流形失配的双基地MIMO虚拟阵列波束优化算法.该方法利用MIMO的波形分集技术与双基地的目标定位解算,通过波达角和波离角在双基地定位椭圆上的对应关系,将双基地MIMO的二维合成方向矢量转化为一维的虚拟阵列流形,并证明了双基地MIMO虚拟阵列的波束图等效为发射波束图在接收阵角度域上的响应和接收波束图的合成.在优化波束图旁瓣的同时,该算法兼顾了固定方向上的相干干扰抑制,并提高了双基地MIMO虚拟阵列波束优化抗阵列流形失配的能力.通过波束图和方位谱优化的仿真分析和水池试验,验证了算法的有效性.     

Transfer effects on communication and collision avoidance behavior from playing a three-dimensional auditory game based on a virtual auditory display

Transfer effects on two everyday auditory skills from playing an auditory game using a virtual auditory display (VAD) were investigated using two groups of 20 normally sighted participants. Representative auditory skills of blindfolded participants were pre-tested using communication and collision avoidance tasks. They performed the tasks two weeks later. Participants in a training group played the VAD-based game on seven days (30 min/day) during the two weeks: control group participants did not. Playing the VAD-based game significantly increased face-contacts in the communication task and significantly improved participants’ collision avoidance. No significant differences were found between the groups’ subjective ratings of the rated tension levels during the communication task or between rated levels for the collision threat. Performance revealed transfer effects of playing the VAD-based game on communication behaviors in social interaction and on avoidance of approaching objects: VAD-based games are effective training tools for auditory skills used in daily life.     

负氢离子源中电子能量沉积三维数值模拟研究

本文理论计算了多峰离子源永磁体, 采用二体碰撞模型(binary collision)处理电子之间的库仑碰撞, 采用空碰撞(null-collision)方法处理电子与氢元素相关粒子, 开发了全三维PIC-MCC模拟算法, 并利用该算法模拟了多峰离子源中电子沉积过程, 分析了多峰磁场对电子的空间和能量分布影响, 结果显示: 电子的非均匀分布起源于高能电子在引出区的B× ▽B漂移.     

Weak signal direction of arrival estimation for colocated multiple-input multiple-output sonar array

In order to suppress the influence of symmetrical noise component on multiple-input multiple-output(MIMO)sonar's direction of arrival(DOA)estimation under the condition of low signal-to-noise ratio,we propose a DOA estimation algorithm based on covariance matrix reconstruction method.Firstly,the noise field can be decomposed into symmetrical noise field and asymmetrical noise field.We utilize symmetry property of colored noise matrix and the feature that the imaginary part of covariance matrix has no relation with the symmetry noise to remove the real part of covariance matrix.This operation helps to suppress the influence of colored noise on DOA estimation accuracy.Based on the principle of the imaginary matrix part displacement and the dimension reduction transformation method,the real part of covariance matrix is reconstructed,which helps to suppress the bilateral spectrum interference.Thereafter,Toeplitz method is applied for the covariance matrix decorrelation amendment,and a noise subspace is formed by singular value decomposition(SVD).Finally,we can estimate the DOA of target signals.Both theoretical analysis results and numerical simulation results verify the symmetrical noise suppression performance of this algorithm,and the estimation performance of target azimuth is improved obviously.This method has the characteristics of lower operational complexity,higher degrees of freedom and stronger target resolution.     

混沌参数调制下RSA数据加密算法研究

随着网络信息通讯技术的发展,信息数据的通讯安全成为信息化网络信息数据通讯过程中的重要问题。面对大量侵入程序与数据漏洞的威胁,一套强有效的数据加密算法成为数据研究领域的研究方向。RSA数据加密算法作为如今常用的安全性最高的算法,在大数据动态数据节点混沌排列的条件下,无法有效保证数据的加密安全。混沌参数下,出现加密逻辑断裂、溢出、数列逆排等严重的算法漏洞。对此,提出混沌参数调制下RSA数据加密算法研究,采用混沌参数特征处理单元、特征序列逻辑控制单元与混沌FIE-RSA算法,对传统RSA加密算法存在的问题进行针对性解决。通过仿真实验证明,提出的混沌参数调制下RSA数据加密算法研究中,采用的一系列方法具有加密处理响应速度快、处理运算时间短、加密安全度高、反破解性能强等特点。