Region coverage-aware path planning for unmanned aerial vehicles: A systematic review

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become prominent in recent years. The UAV-based survey faces several operational issues, such as complicated terrain, limited UAV resources, obstacles, sensor limitations, and other environmental factors. In addition, the coverage plan includes numerous objectives, such as lowering path length, maximizing coverage, and limiting survey time, necessitating multi-objective optimization. UAVs require effective coverage path planning (CPP) to generate the ideal route. It involves determining the path which encompasses every point inside the required region under different constraints. The process automates the process of route determination for autonomous operation by considering various environmental constraints. In this paper, we explore and analyze the existing research on the various techniques used in coverage route planning for UAVs. It provides an overview of the current state-of-the-art CPP methods for UAVs. The study discusses the key challenges and requirements of CPP for UAVs and presents various approaches proposed in the literature to tackle these challenges. We explore a variety of geometric flight patterns for the area of interest having UAV deployment. It also features multi-UAV and multi-region coverage strategies, providing a new dimension to UAV-based operations. The energy consumption of UAVs during CPP is an essential factor, as it influences their flight length and mission duration. The design of the CPP algorithm is determined by the unique requirements of the UAV application, such as the size and form of the region to be mapped, the existence of obstacles, and the desired coverage resolution. Path planning strategies in a three-dimensional environment and dynamic coverage are also included in the study. Moreover, we compare the existing strategies using different performance metrics to evaluate the success of covering missions. Finally, the problems and unresolved concerns related to UAV coverage path planning are examined to provide valuable insights to the readers.     

Integration of multi access edge computing with unmanned aerial vehicles: Current techniques,open issues and research directions

During the last decade, research and development in the field of multi access edge computing (MEC) has rapidly risen to prominence. One of the factors propelling MEC’s evolution is the ability to deploy edge servers capable of providing both communication and computational services in close proximity to the mobile user terminal. MEC has been regarded as a potentially transformative technique for fifth-generation (5G) and beyond 5G (B5G) wireless communication systems, as well as a possible complement to traditional cloud computing. Additionally, unmanned aerial vehicles (UAVs) integrated with MEC will play a critical role by introducing an additional mobility based computational layer to provide more secure, efficient and faster services. UAV enabled MEC offers seamless connectivity, fulfilling the promise of 5G’s ubiquitous connectivity. Due to the enormous interest in UAV enabled MEC, there has been a tremendous increase in the number of published research articles in this domain; however, the research area still lacks a systematic study and categorization. We present a systematic literature review (SLR) on UAV enabled MEC, examining and analyzing data on the current state of the art using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To streamline our assessment, this study analyzes several research papers carefully selected through a multi-stage process satisfying the eligibility criteria defined in the paper. One of the SLR’s primary contributions is to broadly classify the research in the UAV enabled MEC domain into different categories including energy efficiency, resource allocation, security, architecture, and latency. We have identified key findings, technology, and pros and cons for the selected articles under each category. Additionally, we discuss the key open issues related to scalability and fairness, resource allocation and offloading optimization, service delivery with a focus on quality of experience (QoE) and quality of service (QoS), and standardization. Finally, we discuss several future research directions that would address the aforementioned issues and emerging use cases for UAV enabled MEC.     

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.     

激光供能无人机的一种优化跟踪算法

航天事业的发展以及新能源技术的开发,使得小型电动无人机在现代战争、科学研究等方面具有较高的应用价值。激光无线能量传输技术能有效解决小型电动无人机续航时间短的问题,极大提高了无人机的工作能效。以无人机激光供能系统结构原理为基础,针对小型电动无人机激光无线供能的特点,提出了一种最大功率点优化跟踪方法:即采用恒定电压法（CV法）和萤火虫算法（FA法）相结合的优化控制算法,在激光投射到无人机上的光伏电池板上后,通过对无人机激光无线充电过程中最大功率点的跟踪,提高激光利用率及充电稳定性。并且通过数值仿真,验证了所提算法的准确性和适用性。     

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.     

无人机副遥控系统连续波电磁辐照机理

上行副遥控冗余设计是无人机战场生存能力的重要保证,为检验连续波辐照环境下某型无人机副遥控数据链系统的可靠性,基于连续波辐射发射系统建立了无人机连续波辐照效应试验平台,以典型"失锁"效应作为受干扰判别依据,开展了副遥控数据链系统辐照效应研究,利用电磁仿真和注入法分析了"失锁"效应机理和主要干扰耦合路径。试验结果表明:在某些敏感频点,副遥控链路会出现通信中断现象,即表现为失锁状态,最小失锁阈值为0.098V/m,且出现失锁现象的电场阈值随辐射场极化方向改变。经过电磁仿真试验可知,天线是无人机副遥控系统的主要干扰耦合路径,导致副遥控数据链产生失锁的频点均与工作频段相关,电磁能量通过前门耦合传导进入机载收发组合内部,当信噪比达到一定程度时,解调输出误码率过大,通信随即中断,产生失锁。     

Deployment and trajectory design of fixed-wing UAVs in NOMA assisted wireless networks

This paper proposes a deployment and trajectory scheme for fixed-wing unmanned aerial vehicles (UAVs) deployed as flying base stations in multi-UAV enabled non-orthogonal multiple access (NOMA) downlink communication. Specifically, the deployment of UAVs and power allocation of users are jointly optimized to maximize the sum-rate. Thereafter, the energy efficiency maximization problem is formulated to optimize the trajectory of UAVs by jointly considering the quality of service (QoS) requirement of users, various flight constraints, limited on-board energy, and users’ mobility. Initially, the existing users are divided into clusters by k-means clustering, where each cluster is served by a single UAV. Then, the clusters are further divided into multiple sub-clusters, each having a pair of near and far users. Orthogonal multiple access (OMA) is applied among sub-clusters and NOMA is applied to intra sub-cluster users. Lastly, the Balanced-grey wolf optimization (B-GWO) algorithm is proposed for solving the non-convex optimization problems. Simulation results prove the superiority of the B-GWO based deployment and trajectory algorithms compared to the benchmarks. In addition, the proposed B-GWO based trajectory algorithm achieves a near-optimal performance with an optimality gap of less than 1.5% compared to the exhaustive search.     

Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks

Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness.     

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。     

Analysis of fluctuations of antenna pattern in U-V2X communications

The third-generation partnership project (3GPP) defined several scenarios of vehicle-to-everything (V2X) networks such as vehicle-to-vehicle, vehicle-to-pedestrian, and vehicle-to-infrastructure. Unmanned aerial vehicles (UAVs) exploiting such scenarios for vehicular applications like vehicular-platooning, autonomous driving, and traffic management have shown improved networks’ performance in UAV-V2X (U-V2X) communications. This promising performance is typically obtained by considering high directional UAV antennas. However, the UAV vibration makes UAV antennas’ beam-width sensitive to UAV fluctuations which results in degrading the networks’ performance. Thus, in this paper, the performance of a U-V2X network in the presence of UAV fluctuations is investigated. The UAVs are modeled using an independent homogeneous Poisson Point Process and the vehicular-nodes are modeled using an independent Poisson Line Process. To this end, the association probability and success probability of various links such as vehicular-node connected to a UAV link, vehicular-node connected to a near-by vehicular-node link, and the overall U-V2X link are derived. In addition, the effect of different network settings such as the number of vehicular-nodes, UAVs, roads, and antennas is quantified. Moreover, the spectral efficiency of a U-V2X network is evaluated. Extensive simulations revealed that V2X networks facilitate UAVs and that for less dense areas with fewer roads, buildings, and crowds; UAVs provide more reliable links for connectivity than vehicular-nodes. Furthermore, it is also revealed that UAVs provide less reliable connection for V2X networks with larger UAV antenna fluctuations.     

UAV caching in 6G networks: A Survey on models,techniques, and applications

The rapid development road map of 6G networks has posed a new set of challenges to both industrial and academic sectors. On the one hand, it needs more disruptive technologies and solutions for addressing the threefold issues including enhanced mobile broadband, massive machine-type communications, and ultra-reliable and low-latency communications. On the other hand, the ever-massive number of mobile users and Internet of Things devices conveys the huge volume of traffic throughout the 6G networks. In this context, caching is one of the most feasible technologies and solutions that does not require any system architecture changes nor costly investments, while significantly improve the system performance, i.e., quality of service and resource efficiency. Ground caching models deployed at macro base stations, small-cell base stations, and mobile devices have been successfully studied and currently extended to the air done by unmanned aerial vehicles (UAVs) to deal with the challenges of 6G networks. This paper provides a comprehensive survey of UAV caching models, techniques, and applications in 6G networks. In particular, we first investigate the entire picture of caching models moving from the ground to the air as well as the related surveys on UAV communications. Then, we introduce a typical UAV caching system and describe how it works in connection with all types of the transceivers, end users, and applications and services (A&Ss). After that, we present the recent advancements and analyses of the UAV caching models and common system performance metrics. Furthermore, the UAV caching with assisted techniques, UAV caching-enabled mechanisms, and UAV caching A&Ss are discussed to demonstrate the role of UAV caching system in 6G networks. Finally, we highlight the ongoing challenges and potential research directions toward UAV caching in 6G networks.     

Wireless ultraviolet light MIMO assisted UAV direction perception and collision avoidance method

In order to solve the problem of UAV’s direction perception and collision prevention in the limited radio communication environment, we use wireless ultraviolet (UV) communication as an auxiliary communication method in UAVs. We improve the traditional artificial potential field (APF) method, design a UV- MIMO model installed on the UAV, and propose the improved artificial potential field (IAPF) method. On this basis, the determination of the threat source direction in IAPF algorithm is further simplified, and the improved artificial potential field based on ultraviolet MIMO (IAPF-UVMIMO) algorithm is proposed. Experimental results show that IAPF effectively solves the problems of unreachable targets, path oscillations and local minimums that exist in the traditional artificial potential field method. In addition, IAPF-UVMIMO also improves the tolerance of the threat source direction. Compared with IAPF, the number of collisions with actual collision threats is reduced by 22.6%.     

Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.     

高功率电磁脉冲对无人飞行器的毁伤破坏

高功率电磁脉冲干扰甚至损毁无人飞行器是应对小型无人飞行器威胁的一种非常有效的办法,高功率电磁脉冲对无人机系统进行干扰和毁伤主要通过前门耦合和后门耦合的方式。完成了高功率电磁脉冲对无人飞行器的毁伤破坏实验,完成了无人机在电磁脉冲源毁伤作用下的目标易损性分析,根据毁伤效果将高频电磁脉冲对无人飞行器毁伤等级分为三级,即干扰级、毁伤级、失效级。分析了电磁脉冲作用的主要目标元件,结果表明,无人飞行器最有可能受干扰的部分为接收机和电子调速器。在相关研究和实际应用时,应着重研究电磁脉冲对无人飞行器的接收机和电子调速器的毁伤破坏,根据接收机和电子调速器内部的电路结构来确定毁伤最佳频率。     

Secure efficiency maximization for UAV-assisted mobile edge computing networks

This paper considers an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, in which the intelligent reflecting surface (IRS) is applied to enhance the performance of the wireless transmission. The role of the UAV is twofold: (1) It is equipped with a MEC server and receives computing tasks from ground users and IRS at the same time; (2) It sends interference signals to counter the potential eavesdropper. Here, the UAV is working as a full duplex equipment, i.e., sending and receiving meanwhile. We comprehensively considered the flight speed constraint of the UAV, the total mission data constraint and the minimum security rate constraint of multiple users on the ground. The phase matrix constraints of IRS are also considered. Our system is dedicated to maximizing the efficiency of secure computing. The formulated problem is highly non-convex, we consider to propose an alternative optimization algorithm. The simulation results show that the proposed scheme not only achieves higher safe computing efficiency, but also has better performance in terms of energy consumption and security rate.     

Optimal UAV Formation Tracking Control with Dynamic Leading Velocity and Network-Induced Delays

With the rapid development of UAV technology, the research of optimal UAV formation tracking has been extensively studied. However, the high maneuverability and dynamic network topology of UAVs make formation tracking control much more difficult. In this paper, considering the highly dynamic features of uncertain time-varying leader velocity and network-induced delays, the optimal formation control algorithms for both near-equilibrium and general dynamic control cases are developed. First, the discrete-time error dynamics of UAV leader–follower models are analyzed. Next, a linear quadratic optimization problem is formulated with the objective of minimizing the errors between the desired and actual states consisting of velocity and position information of the follower. The optimal formation tracking problem of near-equilibrium cases is addressed by using a backward recursion method, and then the results are further extended to the general dynamic case where the leader moves at an uncertain time-varying velocity. Additionally, angle deviations are investigated, and it is proved that the similar state dynamics to the general case can be derived and the principle of control strategy design can be maintained. By using actual real-world data, numerical experiments verify the effectiveness of the proposed optimal UAV formation-tracking algorithm in both near-equilibrium and dynamic control cases in the presence of network-induced delays.     

无线认知网络中拓扑控制方法研究

作为下一代通信网络,无线认知网络已成为当前的研究热点。由于节点的移动性,无线网络拓扑结构动态变化,拓扑控制一直是无线网络的难点问题。通过借鉴移动自组织网络(MANETS)中的拓扑控制方法,提出了无线认知网络中基于博弈论和认知功能相结合的拓扑控制方法。无线认知节点能够通过主动决策调节自身节点位置,在保证网络连通性的基础上实现网络覆盖面积最大。仿真实验结果验证了方法的有效性和收敛性。     

Secure energy efficiency maximization for dual-UAV-assisted intelligent reflecting surface system

In this paper, we focus on minimizing energy consumption under the premise of ensuring the secure offloading of ground users. We used dual UAVs and intelligent reflective surfaces (IRS) to assist ground users in offloading tasks. Specifically, one UAV is responsible for collecting task data from ground users, and the other UAV is responsible for sending interference noise to counter potential eavesdroppers. The IRS can not only improve the transmission capacity of ground users, but also reduce the acceptance of eavesdroppers. The original problem is strong non-convex, so we consider using the block coordinate descent method. For the proposed sub-problems, we use Lagrangian duality and first-order Taylor expansion to obtain the results, and finally achieve system design through alternate optimization. The simulation results show that our proposed scheme is significantly better than other existing schemes.     

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.     

Analysis of interference cancellation techniques in OFDMA systems employing adaptive antenna arrays

We analyze the problem of interference cancellation in OFDMA cellular communication systems employing adaptive antenna arrays at the receiver and operating in the channels with frequency-selective fading and interference from the neighboring base stations. This problem is very important for modern broadband cellular systems operating with a low coefficient of multiple use of frequencies. Under such conditions of the network deployment, the system efficiency is restricted by the level of intrachannel interference from the neighboring base stations transmitting in the same frequency channel. In this work, we analyze different methods for measuring the interference characteristics and using them for spatial processing of received signals. Five different methods for signal processing in an adaptive antenna array of the receiver are discussed. Each approach (reception method) has its own interference immunity and complexity degree. Comparative analysis of the interference immunity of the considered receivers has been performed for different frequency-channel loads and different numbers of interfering stations. Recommendations for using the considered receivers are given.