Joint Trajectory and Power Optimization for Securing UAV Communications Against Active Eavesdropping

Due to both of jamming and eavesdropping,active eavesdroppers can induce more serious security threats to unmanned aerial vehicle(UAV)-enabled communications.This paper considers a secure UAV communication system including both the downlink(DL)and uplink(UL)transmissions,where the confidential information is transmitted between a UAV and a ground node in the presence of an active eavesdropper.We aim to maximize the average secrecy rates of the DL and UL communications,respectively,by jointly optimizing the UAV trajectory and the UAV/ground node’s transmit power control over a given flight period.Due to the non-convexity of the formulated problems,it is difficult to obtain globally optimal solutions.However,we propose efficient iterative algorithms to obtain high-quality suboptimal solutions by applying the block coordinate descent and successive convex optimization methods.Simulation results show that the joint optimization algorithms can effectively improve the secrecy rate performance for both the DL and UL communications,as compared with other baseline schemes.The proposed schemes can be considered as special cases of UAV-assisted non-orthogonal multiple access(NOMA)networks.

Physical Layer Security for UAV Communications:A Comprehensive Survey

Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an open environment,UAV communications benefit from dominant line-of-sight links;however,this on the other hand renders the communications more vulnerable to malicious attacks.Recently,physical layer security(PLS)has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches.In this paper,a comprehensive survey on the current achievements of UAV-PLS is conducted.We first introduce the basic concepts including typical static/-mobile UAV deployment scenarios,the unique air-toground channel and aerial nodes distribution models,as well as various roles that a UAV may act when PLS is concerned.Then,we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems,and extend the discussion to the more general scenario where the UAVs’mobility is further exploited.For both cases,respectively,we summarize the commonly adopted methodologies,then describe important works in the litera ture in detail.Finally,potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS.

Energy Model for UAV Communications:Experimental Validation and Model Generalization

Wireless communication involving unmanned aerial vehicles(UAVs)is expected to play an important role in future wireless networks.However,different from conventional terrestrial communication systems,UAVs typically have rather limited onboard energy on one hand,and require additional flying energy consumption on the other hand.This renders energy-efficient UAV communication with smart energy expenditure of paramount importance.In this paper,via extensive flight experiments,we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs,and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging,if not impossible.Specifically,we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight.With about 12,000 valid power-speed data points collected,we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature.In addition,in order to exclude the potential bias caused by the theoretical energy model,the obtained measurement data is also trained using a model-free deep neural network.It is found that the obtained curve from both methods can match quite well with the theoretical energy model.Next,we further extend the study to arbitrary 2-dimensional(2-D)flight,where,to our best knowledge,no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed,direction,and acceleration.To fill the gap,we first propose a heuristic energy model for these more complicated cases,and then provide experimental validation based on the measurement results for circular level flight.

AG Channel Measurements and Characteristics Analysis in Hilly Scenarios for 6G UAV Communications

As an important part of sixth generation(6G)integrated space-air-ground-sea networks,unmanned aerial vehicle(UAV)communications have aroused great attention and one of its typical application scenarios is the hilly environments.The related UAV air-ground(AG)channel characteristics analysis is crucial for system design and network evaluation of future UAV communications in hilly scenarios.In this paper,a recently conducted channel measurements campaign in a hilly scenario is presented,which is conducted at the center frequencies of 2.585 GHz and 3.5 GHz for different flight trajectories.Based on the measurement data,some key channel characteristics are analyzed,including path loss(PL),shadow fading(SF),Rician K-factor,root mean square(RMS)delay spread(DS),and temporal auto-correlation function(ACF).Finally,the comparison of typical channel characteristics under circular and straight trajectories is given.The related results can provide a theoretical reference for constructing future UAV communication system in hilly scenarios.

Enabling Efficient Caching in High Mobility UAV Communications Network under Limited Backhaul

Due to flexible deployment,unmanned aerial vehicle(UAV)mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations(TBSs).Different from TBSs,however,UAV access points(UAPs)are of high mobility in horizontal and vertical dimensions,which may deteriorate the coverage performance.Worsestill,the mobility of UAPs would as well increase the pressure of wireless backhaul.In this light,we investigate the performance of the cache-enabled UAV communications network(CUCN)in terms of network spatial throughput(ST)by analyzing the line of sight(LoS)connections and non-line of sight(NLoS)connections.It is found that the network ST is exponentially decreased with the square of UAP altitude.Furthermore,contrary to intuition,a large cache size may deteriorate the network ST when UAPs are over-deployed.The reason is that a large cache size increases the hit probability,which may increase the activation of UAPs and consequently result in complicated interference.Aiming to maximize the network ST,we optimize the cache strategy under limited backhaul.Remarkably,the results show that network ST could be substantially improved by the optimized cache strategy and the performance degeneration brought by UAP high mobility could be even eliminated especially when the UAP altitude is high.

Sensing-Assisted Accurate and Fast Beam Management for Cellular-Connected mmWave UAV Network

Beam management,including initial access(IA)and beam tracking,is essential to the millimeter-wave Unmanned Aerial Vehicle(UAV)network.However,the conventional communicationonly and feedback-based schemes suffer a high delay and low accuracy of beam alignment,since they only enable the receiver to passively“hear”the information of the transmitter from the radio domain.This paper presents a novel sensing-assisted beam management approach,the first solution that fully utilizes the information from the visual domain to improve communication performance.We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction.Besides,we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments.Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay,association accuracy,tracking error,and communication performance.

DQN-based decentralized multi-agent JSAP resource allocation for UAV swarm communication

It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.

Configurable Virtual MIMO via UAV Swarm:Channel Modeling and Spatial Correlation Analysis

Air-to-ground wireless channel modeling for unmanned aerial vehicle(UAV)communications has been widely studied.However,channel modeling for UAV swarm-enabled cooperative communication still needs investigation,where the impact of UAV positions on the spatial channel characteristics is of particular importance.In this paper,we consider a UAV swarm-enabled virtual multiple input multiple output(MIMO)system,where multiple single-antenna UAVs cooperatively transmit to multiple ground users(GUs).We establish a common coordinate system,as well as a UAV swarm-oriented coordinate system,to describe the relative positions of the GUs and the UAV elements,respectively.Based on the established coordinate systems,geometric ray superposition method is applied to describe the spatial channel matrix.The proposed modeling framework can be directly used to describe the line-of-sight and two-ray propagations,and can be extended for including more practical spatial features such as multipath scattering,inter-UAV blockage,and random UAV jittering,etc.Based on the proposed model,we further analyze the spatial correlation among the virtual MIMO links of GUs located at different positions.Via extensive simulations,we show that thanks to the flexible deployment of UAVs,the virtual MIMO array structure can be conveniently configured to get desired channel properties,such as the channel capacity,eigenvalue and condition number distribution,and spatial correlation distribution.This shows the possibility and importance of exploiting a new design dimension,i.e.,the UAV swarm pattern,in such cooperative virtual MIMO systems.

Secrecy Capacity Maximization for a UAV-Assisted MEC System

Unmanned aerial vehicle(UAV)communication has attracted wide attentions in the mobile edge computing(MEC)system owing to its high-flexibility and simple operation auxiliary communication mode.Users can offload computing tasks to UAVs,which serves as edge nodes.Meanwhile,UAVs forward the tasks onto a cloud center or base station for processing,thereby shortening the implementation time of tasks.Nevertheless,the offloading links of an UAV-assisted MEC system adopt a radio broadcasting mode.Several eavesdroppers might be present in the environment to eavesdrop the data sent by users and UAVs,thereby causing significant effects on the secrecy performance.An optimized iterative algorithm is proposed in this paper to realize the maximum secrecy capacity of the MEC system and further improve the secrecy performance of an UAV-assisted MEC system and assure secrecy transmit.By doing so,the secrecy transmit problems of the two-staged offloading model of the UAV-assisted MEC system are solved.The maximum secrecy capacity of the system is obtained through joint optimization of the UAV positions,transmit power of the UAV,task offloading ratio,and allocation of offloading users considering the limited time and energy of an UAV.Simulation results demonstrate that the proposed iterative algorithm can effectively improve the secrecy capacity of the system.

A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks

As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.

Joint 3D Trajectory and Resource Optimization for A UAV Relay-Assisted Cognitive Radio Network

In this paper,we consider a new spectrum sharing scenario for a cognitive relay network,where a secondary unmanned aerial vehicle(UAV)relay receives information from the ground secondary base station(SBS)and transmits information to the ground secondary user(SU),coexisting with the primary users(PUs)at the same wireless frequency band.We investigate the optimization of the UAV relay’s three-dimensional(3D)trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs.The information throughput maximization problem is studied by jointly optimizing the UAV relay’s 3D trajectory and the transmit power of the SBS and the UAV,subject to the constraints on the velocity and elevation of the UAV relay,the maximum and average transmit power,and the information causality,as well as a set of interference temperature(IT)constraints.An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique,the successive convex approximation technique and the alternating optimization method.Finally,simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput。

An Efficient Trajectory Planning for Cellular-Connected UAV under the Connectivity Constraint

Unmanned Aerial Vehicles(UAVs)acting as aerial users to access the cellular network form a promising solution to guarantee its safe and efficient operations via the high-quality communication.Due to the flexible mobility of UAVs and the coverage range limits of ground base station(GBS),the signalto-noise ratio(SNR)of the communication link between UAVs and GBS will fluctuate.It is an important requirement to maintain the UAV’s cellular connection to meet a certain SNR requirement during the mission for UAV flying from take off to landing.In this paper,we study an efficient trajectory planning method that can minimize a cellular-connected UAV’s mission completion time under the connectivity requirement.The conventional method to tackle this problem adopts graph theory or a dynamic programming method to optimize the trajectory,which generally incurs high computational complexities.Moreover,there is a nonnegligible performance gap compared to the optimal solution.To this end,we propose an iterative trajectory optimizing algorithm based on geometric planning.Firstly,we apply graph theory to obtain all the possible UAV-GBS association sequences and select the candidate association sequences based on the topological relationship among UAV and GBSs.Next,adopting the triangle inequality property,an iterative handover location design is proposed to determine the shortest flight trajectory with fast convergence and low computation complexity.Then,the best flight trajectory can be obtained by comparing all the candidate trajectories.Lastly,we revealed the tradeoff between mission completion time and flight energy consumption.Numerical results validate that our proposed solution can obtain the effectiveness with set accuracy and outperform against the benchmark schemes with affordable computation time.

Predictive Decision and Reliable Accessing for UAV Communication in Space-Air-Ground Integrated Networks

The cooperation of multiple Unmanned Aerial Vehicles(UAVs) has become a promising scenario in Space-Air-Ground Integrated Networks(SAGINs) recently due to their widespread applications,where wireless communication is a basic necessity and is normally categorized into control and nonpayload communication(CNPC) as well as payload communication. In this paper, we attempt to tackle two challenges of UAV communication respectively on establishing reliable CNPC links against the high mobility of UAVs as well as changeable communication conditions, and on offering dynamic resource optimization for Quality-of-Service(QoS) guaranteed payload communication with variable link connectivity. Firstly, we propose the concept of air controlling center(ACC), a virtual application equipped on the infrastructure in SAGINs, which can collect global information for estimating UAV trajectory and communication channels. We then introduce the knapsack problem for modelling resource optimization of UAV communication in order to provide optimal access points for both CNPC and payload communication. Meanwhile, using the air controlling information, predictive decision algorithm and handover strategy are introduced for the reliable connection with multiple access points. Simulation results demonstrate that our proposal ensures an approximate always-on reliable accessing of communication links and outperforms the existing methods against high mobility,sparse distribution, and physical obstacles.

Efficient Deployment of Multiple UAVs for IoT Communication in Dynamic Environment

The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.

Energy-Efficient Full-Duplex UAV Relaying with Trajectory Optimization and Power Control in Maritime Communication Environments

This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration,the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes.The optimization problem is non-convex and thus cannot be solved directly.Therefore,it is decoupled into two subproblems.One sub-problem is for the transmit power control at the source and the UAV relay nodes,and the other aims at optimizing the UAV s flight trajectory.By using the Lagrangian dual and Dinkelbach methods,the two sub-problems are solved,leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization.Computer simulations demonstrated that by conducting the proposed algorithm,the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions,and the proposed algorithm can achieve signiflcantly higher energy efficiency as compared with the other benchmark schemes.

Sum-rate maximization for UAV-enabled two-way relay systems

In this paper,an Unmanned Aerial Vehicle(UAV)-enabled two-way relay system with Physical-layer Network Coding(PNC)protocol is considered.A rotary-wing UAV is applied as a mobile relay to assist two ground terminals for information interaction.Our goal is to maximize the sum-rate of the two-way relay system subject to mobility constraints,propulsion power consumption constraints,and transmit power constraints.The formulated problem is not easy to solve directly because it is a mixed integer non-convex optimization problem.Therefore,we decompose it into three sub-problems,and use the mutation arithmetic of the Genetic Algorithm(GA)and Successive Convex Approximation(SCA)to dispose.Besides,a high-efficiency iterative algorithm is proposed to obtain a locally optimal solution by jointly optimizing the time slot pairing,the transmit power allocation,and the UAV trajectory design.Numerical results demonstrate that the proposed design achieves significant gains over the benchmark designs.

Channel Knowledge Map(CKM)-Assisted Multi-UAV Wireless Network:CKM Construction and UAV Placement

Channel knowledge map(CKM)has recently emerged as a viable new solution to facilitate the placement and trajectory optimization for unmanned aerial vehicle(UAV)communications,by exploiting the siteand location-specific radio propagation information.This paper investigates a CKM-assisted multi-UAV wireless network,by focusing on the construction and utilization of CKMs for multi-UAV placement optimization.First,we consider the CKM construction problem when data measurements for only a limited number of points are available.Towards this end,we exploit a data-driven interpolation technique,namely the Kriging method,to construct CKMs to characterize the signal propagation environments.Next,we study the multi-UAV placement optimization problem by utilizing the constructed CKMs,in which the multiple UAVs aim to optimize their placement locations to maximize the weighted sum rate with their respectively associated ground base stations(GBSs).However,the weighted sum rate function based on the CKMs is generally non-differentiable,which renders the conventional optimization techniques relying on function derivatives inapplicable.To tackle this issue,we propose a novel iterative algorithm based on derivative-free optimization,in which a series of quadratic functions are iteratively constructed to approximate the objective function under a set of interpolation conditions,and accordingly,the UAVs’placement locations are updated by maximizing the approximate function subject to a trust region constraint.Finally,numerical results are presented to validate the performance of the proposed designs.It is shown that the Kriging method can construct accurate CKMs for UAVs.Furthermore,the proposed derivative-free placement optimization design based on the Kriging-constructed CKMs achieves a weighted sum rate that is close to the optimal exhaustive search design based on ground-truth CKMs,but with much lower implementation complexity.In addition,the proposed design is shown to significantly outperform other benchmark schemes.

A Secure Communication Protocol for Unmanned Aerial Vehicles

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles.Multiple UAVs and autopilot systems support it,and it provides bi-directional communication between the UAV and Ground Control Station.The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS.In order to increase the transfer speed and efficiency,the Mavlink does not encrypt the messages.As a result,the protocol is vulnerable to various security attacks such as Eavesdropping,GPS Spoofing,and DDoS.In this study,we tackle the problem and secure the Mavlink communication protocol.By leveraging the Mavlink packet’s vulnerabilities,this research work introduces an experiment in which,first,the Mavlink packets are compromised in terms of security requirements based on our threat model.The results show that the protocol is insecure and the attacks carried out are successful.To overcomeMavlink security,an additional security layer is added to encrypt and secure the protocol.An encryption technique is proposed that makes the communication between the UAV and GCS secure.The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency.The results are validated in terms of transfer speed,performance,and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol.Our achieved results have significant improvement over the literature and Mavlink in terms of security.

Robust trajectory planning for UAV communication systems in the presence of jammers

Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.

Resource Allocation in UAV-NOMA Communication Systems Based on Proportional Fairness

This paper investigates the power allocation problem in non-orthogonal multiple access(NOMA)integrated unmanned aerial vehicle(UAV)communication systems.In particular,we propose a novel resource allocation scheme to increase the transmission rate of the users that have relatively worse channel state information,while reducing the sum rate loss.To solve this problem efficiently,we decouple the optimization problem into three subproblems.First,we solve the problem of user pairing and subchannel allocation.Second,the optimum power proportional factor is derived to allocate transmit power among different users on the same subchannel.At last,different subchannels are allocated with appropriate power to improve the performance of the subchannels.Simulation results show that the proposed scheme can enjoy a better performance than the benchmark methods since it can achieve a proper trade-off between the system sum rate and the proportional fairness.