A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks

With the increase in capacity demands and the requirement of ubiquitous coverage in the fifth generation and beyond wireless communications networks, unmanned aerial vehicles (UAVs) have acquired a great attention owing to their outstanding characteristics over traditional base stations and relays. UAVs can be deployed faster and with much lower expenditure than ground base stations. In addition, UAVs can enhance the network performance thanks to their strong line-of-sight link conditions with their associated users and their dynamic nature that adapts to varying network conditions. Optimization of the UAV 3D locations in a UAV-assisted wireless communication network was considered in a large body of research as it is a critical design issue that greatly affects communication performance. Although the topic of UAV placement optimization was considered in few surveys, these surveys reviewed only a small part of the growing literature. In addition, the surveys were brief and did not discuss many important design issues such as the objectives of the optimization problem, the adopted solution techniques, the air-to-ground channel models, the transmission media for access and backhaul links, the limited energy nature of the UAV on-board batteries, co-channel interference and spectrum sharing, the interference management, etc. Motivated by the importance of the topic of UAV placement optimization as well as the need for a detailed review of its recent literature, we survey 100 of the recent research papers and provide in-depth discussion to fill the gaps found in the previous survey papers. The considered research papers are summarized and categorized to highlight the differences in the deployment scenario and system model, the optimization objectives and parameters, the proposed solution techniques, and the decision-making strategies and many other points. We also point to some of the existing challenges and potential research directions that have been considered in the surveyed literature and that requires to be considered     

Game theory-based radio resource allocation in NOMA vehicular communication networks supported by UAV

Vehicular communication networks are emerging as a promising technology to provide high-quality internet service such as entertainment for road users via infrastructure-to-vehicle (I2V) communication, and to guarantee road users’ safety via vehicle-to-vehicle (V2V) communication. Some technical issues that impact the performance of these networks are the lack of or poor communication paths between vehicles, and the limitation of radio resources. Unmanned aerial vehicles (UAVs) as promising solutions for supporting vehicular networks could provide communication coverage in hazardous environments and areas with no capacities for installation or maintenance of ground base stations (BSs). Also, non-orthogonal multiple access (NOMA) methods can improve spectral and energy efficiency and thereby allow more users to be connected to the desired network. In this paper, exploring the NOMA, we develop a scheme for optimum resource allocation in presence of a UAV that supports vehicular communications. Resource allocation for this scenario is formulated as a mixed-integer non-linear programming (MINLP) problem. Due to the high complexity of such problems, we propose two low-complexity near-optimal methods. First, we apply difference-of-concave-functions (DC) approximations to solve the problem in an iterative process. Next, we use Stackelberg game-based method for efficient solving, and then, closed-form expressions of optimal power allocations using KKT-conditions are derived. Simulations illustrate the effectiveness of the proposed scheme along with the Stackelberg game-based method.     

Improving robustness of complex networks via the effective graph resistance

Improving robustness of complex networks is a challenge in several application domains, such as power grids and water management networks. In such networks, high robustness can be achieved by optimizing graph metrics such as the effective graph resistance, which is the focus of this paper. An important challenge lies in improving the robustness of complex networks under dynamic topological network changes, such as link addition and removal. This paper contributes theoretical and experimental findings about the robustness of complex networks under two scenarios: (i) selecting a link whose addition maximally decreases the effective graph resistance; (ii) protecting a link whose removal maximally increases the effective graph resistance. Upper and lower bounds of the effective graph resistance under these topological changes are derived. Four strategies that select single links for addition or removal, based on topological and spectral metrics, are evaluated on various synthetic and real-world networks. Furthermore, this paper illustrates a novel comparison method by considering the distance between the added or removed links, optimized according to the effective graph resistance and the algebraic connectivity. The optimal links are different in most cases but in close proximity.     

权重分布对加权网络效率的影响

加权网络可以对复杂系统的相互作用结构提供更加细致的刻画,而改变边权也成为调整和改善网络性质与功能的新途径.基于已有无权网络的效率概念,文中给出了相似权和相异权网络的网络效率定义,并研究了权重分布对于网络效率的影响.从平权的规则网络出发,通过改变权重的分布形式考察权重分布对网络效率的影响,结果发现,在规则网络上,权重分布随机性的增加提高了网络效率,而在几种常见的权重分布形式中,指数分布对网络效率的改进最为显著.同时,权重随机化之后网络最小生成树的总权重减小,意味着网络的运输成本随着权重异质性的增加而降低.以上结果为深入理解权重对网络结构与功能的影响提供了基础. 关键词： 复杂网络 加权网络 权重 网络效率     

Link prediction in complex networks via modularity-based belief propagation

Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes' attributes of the network. Under the assumption that the likelihood of the existence of a link between two nodes can be captured by nodes' similarity, several methods have been proposed to compute similarity directly or indirectly, with information on node degree. However, correctly predicting links is also crucial in revealing the link formation mechanisms and thus in providing more accurate modeling for networks. We here propose a novel method to predict links by incorporating stochastic-block-model link generating mechanisms with node degree. The proposed method first recovers the underlying block structure of a network by modularity-based belief propagation, and based on the recovered block structural information it models the link likelihood between two nodes to match the degree sequence of the network. Experiments on a set of real-world networks and synthetic networks generated by stochastic block model show that our proposed method is effective in detecting missing, spurious or evolving links of networks that can be well modeled by a stochastic block model. This approach efficiently complements the toolbox for complex network analysis, offering a novel tool to model links in stochastic block model networks that are fundamental in the modeling of real world complex networks.     

Analyzing the coexistence of DSRC and Wi-Fi networks using the Poisson line Cox process

Vehicular networks can aid in traffic monitoring, autonomous driving, and car accidents prevention. Yet, the deployment of these networks has been delayed due to the limited spectrum, especially for the case of unlicensed operations. To handle this issue, the Federal Communications Commission (FCC) proposed to permit Wi-Fi devices to operate in the 5.9 GHz band allocated to the intelligent transportation system (ITS). In a recent work, we analyzed the impact of the coexistence of dedicated short range communications (DSRC) and Wi-Fi on future DSRC network deployments by developing a stochastic geometry analytical model that considers a dynamic medium access probability (MAP) of DSRC nodes which uses carrier sense multiple access with collision avoidance (CSMA/CA). This previous work was based on the standard 2D homogeneous Poisson Point Process (PPP) model. In this work, we model the roads using the more applicable but more complex Poisson line process (PLP) Cox point process. We generate performance metrics represented through coverage probability and area system throughput, and we compare these results to our earlier work. The importance of this work is two-fold. First, it allows a further understanding of the impact of DSRC-Wi-Fi coexistence on future DSRC network deployments, and second, it highlights the effectiveness of the PLP in modeling the distribution of vehicles in an area by producing more accurate performance results.     

Proactive eavesdropping rate maximization with UAV spoofing relay

Spoofing relay is an effective way for legitimate agencies to monitor suspicious communication links and prevent malicious behaviors. The unmanned aerial vehicles(UAVs)-assisted wireless information surveillance system can virtually improve proactive eavesdropping efficiency thanks to the high maneuverability of UAVs. This paper aims to maximize the eavesdropping rate of the surveillance system where the UAV is exploited to actively eavesdrop by spoofing relay technology. We formulate the problem to jointly optimize the amplification coefficient, splitting ratio, and UAV’s trajectory while considering successful monitoring. To make the nonconvex problem tractable, we decompose the problem into three sub-problems and propose a successive convex approximation based alternate iterative algorithm to quickly obtain the near-optimal solution. The final simulation results show that the UAV as an active eavesdropper can effectively increase the information eavesdropping rate than a passive eavesdropper.     

Performance evaluation of survivable WDM based SANs in a metro ring network

Storage area networks (SANs) are an essential part of enterprise computing today. There is no comprehensive business continuity plan without SANs in the picture. This paper proposes and evaluates network survivability of optical paths in wavelength division multiplexing (WDM) based storage area networks in a metro ring network. The paper begins with a background on network survivability in metro WDM ring network. Subsequently, the network and node architectures along with their medium access control (MAC) protocols are described. In this work, one link failure (a single cable cut) in metro WDM based SANs in a ring network architecture is considered. Performance evaluation for network survivability in metro WDM SAN is carried out by means of discrete-event computer simulation. Network throughput and packet delay are investigated. The network performance is evaluated for asymmetric (unbalanced) traffic scenarios under Poisson and self-similar traffic.     

Physical layer secrecy analysis of HS-UAV NOMA network with spatially random eavesdroppers

This work investigates the physical layer secrecy performance of a hybrid satellite/unmanned aerial vehicle (HS-UAV) terrestrial non-orthogonal multiple access (NOMA) network, where one satellite source intends to make communication with destination users via a UAV relay using NOMA protocol in the existence of spatially random eavesdroppers. All the destination users randomly distributed on the ground comply with a homogeneous Poisson point process in the basis of stochastic geometry. Adopting Shadowed-Rician fading in satellite-to-UAV and satellite-to-eavesdroppers links while Rayleigh fading in both UAV-to-users and UAV-to-eavesdroppers links, the theoretical expressions for the secrecy outage probability (SOP) of the paired NOMA users are obtained based on the distance-determined path-loss. Also, the asymptotic behaviors of SOP expressions at high signal-to-noise ratio (SNR) regime are analyzed and the system throughputs of the paired NOMA users are examined for gaining further realization of the network. Moreover, numerical results are contrasted with simulation to validate the theoretical analysis. Investigation of this work shows the comparison of SOP performance for the far and near user, pointing out the SOP performance of the network depends on the channel fading, UAV coverage airspace, distribution of eavesdroppers and some other key parameters.     

Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.     

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.     

Blocking in wavelength-routed all-optical WDM network with wavelength conversion

The blocking probability in wavelength-routed all optical networks is very important measure of performance of the network, which can be affected by many factors such as network topology, traffic load, number of links, algorithms employed and whether wavelength conversion is available or not. In this paper, we have proposed a mathematical model to reduce the blocking probability of the WDM optical network for wavelength-convertible networks. The model can be used to evaluate the blocking performance of any network topology also it can be useful to improve its blocking performance of the given network topology. The blocking probability variation of the network for a particular load (per link) has been studied based on the load variation and total number of wavelengths used in the network. This model gives good results for high load (per link).     

相依网络上基于相连边的择优恢复算法

如何有效地应对和控制故障在相依网络上的级联扩散避免系统发生结构性破碎,对于相依网络抗毁性研究具有十分重要的理论价值和现实意义.最新的研究提出一种基于相依网络的恢复模型,该模型的基本思想是通过定义共同边界节点,在每轮恢复阶段找出符合条件的共同边界节点并以一定比例实施恢复.当前的做法是按照随机概率进行选择.这种方法虽然简单直观,却没有考虑现实世界中资源成本的有限性和择优恢复的必然性.为此,针对相依网络的恢复模型,本文利用共同边界节点在极大连通网络内外的连接边数计算边界节点的重要性,提出一种基于相连边的择优恢复算法(preferential recovery based on connectivity link,PRCL)算法.利用渗流理论的随机故障模型,通过ER随机网络和无标度网络构建的不同结构相依网络上的级联仿真结果表明,相比随机方法和度数优先以及局域影响力优先的恢复算法,PRCL算法具备恢复能力强、起效时间早且迭代步数少的优势,能够更有效、更及时地遏制故障在网络间的级联扩散,极大地提高了相依网络遭受随机故障时的恢复能力.     

A greedy-model-based reinforcement learning algorithm for Beyond-5G cooperative data collection

Data collection is an essential part of Beyond-5G and Internet of Things applications. In urban area, heterogeneous access points such as Wi-Fi routers and base stations can meet the required communication coverage and bandwidth in data collection processes. However, in remote area, without communication infrastructures, it is hard to guarantee the communication quality of a large-scale data aggregation network. An existing approach is to use an unmanned aerial vehicle (UAV) to act as a mobile sink to perform data collection and increase the coverage of intelligent wireless sensing and communications. The efficiency and the reliability of such a UAV-assisted data collection system can be significantly enhanced with an intelligent cooperative strategy for the sensors deployed in the field to communicate with the UAV. Furthermore, an energy-efficient trajectory planning algorithm is crucial to address the physical limitations of the UAV in this application. In this paper, a data collection process is modeled as a Markov decision process (MDP). The paper begins with proposing two heuristic greedy algorithms, namely distance-greedy (DG) algorithm and rate-greedy (RG) algorithm, which are designed based on prior knowledge of the system and can guarantee the completion of the data collection process in a remote area without the help of fixed communication infrastructures. Based on the outcomes, a multi-agent greedy-model-based reinforcement learning (MG-RL) algorithm is proposed, which specifically designs the environmental state and the reward scheme, and introduces multiple UAVs with different parameters to explore environments in parallel to accelerate the training. In conclusion, the two proposed greedy algorithms have lower complexity of implementation while the proposed MG-RL algorithm yields practical UAVs’ flight trajectories and shortens the time for completing a data collection task.     

Synchronization in complex clustered networks

Synchronization in complex networks has been an active area of research in recent years. While much effort has been devoted to networks with the small-world and scale-free topology, structurally they are often assumed to have a single, densely connected component. Recently it has also become apparent that many networks in social, biological, and technological systems are clustered, as characterized by a number (or a hierarchy) of sparsely linked clusters, each with dense and complex internal connections. Synchronization is fundamental to the dynamics and functions of complex clustered networks, but this problem has just begun to be addressed. This paper reviews some progress in this direction by focusing on the interplay between the clustered topology and network synchronizability. In particular, there are two parameters characterizing a clustered network: the intra-cluster and the inter-cluster link density. Our goal is to clarify the roles of these parameters in shaping network synchronizability. By using theoretical analysis and direct numerical simulations of oscillator networks, it is demonstrated that clustered networks with random inter-cluster links are more synchronizable, and synchronization can be optimized when inter-cluster and intra-cluster links match. The latter result has one counterintuitive implication: more links, if placed improperly, can actually lead to destruction of synchronization, even though such links tend to decrease the average network distance. It is hoped that this review will help attract attention to the fundamental problem of clustered structures/synchronization in network science.      

UAV path design with connectivity constraint based on deep reinforcement learning

Cellular networks are expected to communicate effectively with unmanned aerial vehicles (UAVs) and support various applications. However, existing cellular networks are primarily designed to cover users on the ground; thus, coverage holes in the sky will exist. In this paper, we investigate the problem of path design for cellular-connected UAVs, taking into account the interruption performance throughout the UAV mission to minimize the completion time. Two types of connectivity constraints requirements are assumed to be available. The first is defined as the maximum continuous time interval that the UAV loses connection with base stations (BSs) below a predefined threshold. For the second, we consider the sum outage of UAV is limited during the entire UAV mission. The UAV is tasked with flying from a starting location to a final destination while minimization the mission time, satisfying the two constraints, separately. The formulated path design problem which involves continues variables and a dynamic radio environment, is not convex and thus is extremely difficult to solve directly. To tackle this challenge, a deep reinforcement learning (DRL) based trajectory design algorithm is proposed, where the Dueling Double Deep Q Network(Dueling DDQN) with multi-steps learning method is applied. Simulation results demonstrate the effectiveness of the proposed DRL algorithm and achieve a trade-off between the trajectory length of the UAV and connection quality.     

Some limitations on optical communication reliability through Kolmogorov and non-Kolmogorov turbulence

In free-space optical communication links, atmospheric turbulence causes fluctuations in both the intensity and the phase of the received signal, affecting link performance.Influence of Kolmogorov and non-Kolmogorov turbulence statistics on laser communication links are analyzed for different propagation scenarios, and effects of different turbulence spectrum models on optical communication links are presented. Statistical estimates of the communication parameters such as the probability of fade (miss) exceeding a threshold dB level, the mean number of fades, and BER are derived and examples provided. The presented quantitative data suggest that the non-Kolmogorov turbulence effects on lasercom channels are more severe in many situations and need to be taken into account in wireless optical communication. Non-Kolmogorov turbulence is especially important for elevations above the boundary layer as well as for even low elevation paths over water.     

Modelling of weighted evolving networks with community structures

Many social and biological networks consist of communities–groups of nodes within which links are dense but among which links are sparse. It turns out that most of these networks are best described by weighted networks, whose properties and dynamics depend not only on their structures but also on the link weights among their nodes. Recently, there are considerable interests in the study of properties as well as modelling of such networks with community structures. To our knowledge, however, no study of any weighted network model with such a community structure has been presented in the literature to date. In this paper, we propose a weighted evolving network model with a community structure. The new network model is based on the inner-community and inter-community preferential attachments and preferential strengthening mechanism. Simulation results indicate that this network model indeed reflect the intrinsic community structure, with various power-law distributions of the node degrees, link weights, and node strengths.     

Exploring cooperative NOMA assisted hybrid visible light and radio frequency for enhanced vehicular message dissemination at road intersections

Vehicle-to-everything (V2X) communication aims to achieve significantly improved safety and traffic efficiency, more particularly at road intersection where high percentage of accidents usually occur. The existing vehicular radio frequency (V-RF) based V2X utilizes relaying for improving safety message dissemination at road intersections. For a high traffic density scenario, the V-RF communication with relaying solution may suffer from large latency and low packet delivery rates due to channel congestion. In this paper, we explore cooperative non-orthogonal multiple access (NOMA) communication assisted hybrid vehicular visible light communication (V-VLC) and V-RF communication for improving safety message dissemination and enabling massive connectivity among vehicles for road intersection scenarios. We develop a stochastic geometry based analytical framework to model cooperative NOMA (C-NOMA) transmissions subject to interference imposed by other vehicles on roads. We also examine the impact of vehicles headlights radiation pattern viz. Lambertian and empirical path loss models on statistical characterization of the proposed C-NOMA supported hybrid solution. Our numerical findings reveal that C-NOMA assisted hybrid V-VLC/V-RF system leads to considerable improvement in outage performance and average achievable rate as compared to traditional V-RF solution with relaying. Interestingly, Lambertian model offers a lower outage and higher average achievable rate compared to the empirical model for the proposed hybrid solution. Further, we observe the performance improvement using maximal ratio combining (MRC) considering NOMA transmission for the proposed hybrid solution. The presented framework may serve as an alternative for cooperative intelligent transportation system (C-ITS) to meet diverse application needs for beyond 5G (B5G) V2X networks.     

基于网络编码的无人机数据链技术研究

为了提高无人机数据链网络的传输性能,针对其具有的高动态、多中继和大容量等显著特点,提出了对流链路网络编码策略。通过分析多无人机协同执行任务时形成的网络拓扑结构,利用网络仿真平台NS2建立了高动态多跳移动自组网模型,分别改变模型中节点的移动速度、跳数和数据包大小,观察其对网络吞吐率的影响。仿真结果表明,对流链路网络编码策略能够有效地改善仿真模型的传输性能,很好的应用在无人机数据链网络中。