Unmanned Aerial Vehicle Trajectory Research Articles

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.

UAV Trajectory and Energy Efficiency Optimization in RIS-Assisted Multi-User Air-to-Ground Communications Networks

An air-to-ground downlink communication network consisting of a reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) is proposed. In conjunction with a resource allocation strategy, the system’s energy efficiency is improved. Specifically, the UAV equipped with a RIS starts from an initial location, and an energy-efficient unmanned aerial vehicle deployment (EEUD) algorithm is deployed to jointly optimize the UAV trajectory, RIS phase shifts, and BS transmit power, so as to obtain a quasi-optimal deployment location and hence improve the energy efficiency. First, the RIS phase shifts are optimized by using the block coordinate descent (BCD) algorithm to deal with the nonconvex inequality constraint, and then integrated with the Dinkelbach algorithm to address the resource allocation problem of the BS transmit power. Finally, for solving the UAV trajectory optimization problem, the complex objective function is transformed into a convex function, and the optimal UAV flight trajectory is obtained. Our simulation results show that the quasi-optimal deployment location obtained by the EEUD algorithm is superior to other deployment strategies in energy efficiency. Moreover, the instantaneous energy efficiency of the UAVs along the trajectory of searching the deployment location is better than other comparison trajectories. Furthermore, the RIS-assisted multi-user air-to-ground communication network can offer up to 145% improvement in energy efficiency over the traditional amplify-and-forward (AF) relay.

Sum Rate Optimization for Multiple Access in Multi-FD-UAV-Assisted NOMA-Enabled Backscatter Communication Network

With the rapid development of the Internet of Things (IoT) network, research on low-power and energy-saving devices has attracted extensive attention from both academia and the industry. Although the backscatter devices (BDs) that utilize the environmental power to activate circuits and transmit signals are a promising technology to be deployed as IoT nodes, it is challenging to design a flexible data backhaul scheme for massive BDs. Therefore, in this paper, we consider an unmanned-aerial-vehicle (UAV)-assisted backscatter communication network, where BDs are served by multiple full-duplex (FD) UAVs with the non-orthogonal multiple access (NOMA) schemes and modulate their signals on the downlink signals, which are generated by the UAVs to serve the coexisting regular user equipments (UEs). To maximize the sum rate of the considered system, we construct an optimization problem to optimize the reflection coefficient of BDs, the downlink and the backhaul transmission power, and the trajectory of UAVs jointly. Since the formulated problem is a non-convex optimization problem and is difficult to solve directly, we decouple the original problem into three sub-problems and solve them with the successive convex approximation (SCA) method, thereby addressing the original problem by a block coordinate descent (BCD)-based iterative algorithm. The simulation results show that, compared with the benchmark schemes, the proposed algorithm can obtain the highest system sum rate and utilize limited time-frequency resources more efficiently.

Motion Planning in UAV-Aided Data Collection with Dynamic Jamming

Unmanned-aerial-vehicle (UAV)-aided data collection for Internet of Things applications has attracted increasing attention. This paper investigates motion planning for UAV collecting low-power ground sensor node (SN) data in a dynamic jamming environment. We targeted minimizing the flight energy consumption via optimization of the UAV trajectory while considering the indispensable constraints which cover the collection data demodulation threshold, obstacle avoidance, data collection volume, and motion principle. Firstly, we formulate the UAV-aided data collection problem as an energy consumption minimization problem. To solve this nonconvex optimization problem, we rewrite the original problem by introducing relaxation variables and constructing equivalence constraints to obtain a new relaxation convex problem, which can be solved iteratively using the successive convex approximation (SCA) method. However, SCA is susceptible to initial values, especially in dynamic environments where fixed initial values may lead to a wide range of results, making it difficult to obtain a truly optimal solution to the optimization problem. To solve the initial value problem in dynamic environments, we further propose a communication-flight-corridor(CFC)-based initial path generation method to improve the reliability and convergence speed of the SCA method by constructing reliable communication regions and resilient secure paths in real time. Finally, simulation results validate the performance of the proposed algorithm compared to the benchmark algorithms under different parameter configurations.

General Outage Probability Model for UAV-to-UAV links in Multi-UAV Networks

Unmanned Aerial Vehicles (UAVs) swarms have become increasingly ubiquitous in the civilian domains due to their maneuverability and scalability in collaborative flying missions. However, there is a lack of characterization of wireless communication performance in multi-UAV system without being constrained by the node spatial distributions. In this paper, the performance of a UAV swarm network is analyzed by characterizing the effects of the UAV-to-UAV (U2U) interference in terms of the Signal-to-Interference-plus-Noise Ratio (SINR). To this end, a closed-form analytical model for the Cumulative Distribution Function (CDF) of the SINR is derived by considering both deterministic and stochastic channel processes, as well as deterministic UAV mobilities. The derived model can be used to analyze U2U interference in a UAV swarm network for any arbitrary node locations. Using this model, the effects of the network parameters on various network Key Performance Indicators (KPIs) such as the outage probabilities and channel capacity rate are studied. The model is extended to incorporate hybrid LoS channels, and the general model is used to evaluate the temporal evolutions of network outage probability given the UAV trajectories. With this, the main advantage of the model in analyzing the baseline outage probabilities of multi-UAV deployments is showcased.

Average AoI minimization for data collection in UAV-enabled IoT backscatter communication systems with the finite blocklength regime

Thanks to the autonomy of unmanned aerial vehicle (UAV), UAV-enabled data collection in Internet of Things (IoT) networks has become a key application for the next generation communication network. In this paper, we consider a scenario where an UAV is responsible for collecting data from sensor equipments (SEs) one by one and finally carrying the collected data to the computation center for processing. Different from the commonly used assumption that SEs always generate data at the beginning of each time slot, it is assumed that SEs can generate data at any instant during one time slot, which is more practical. Since SEs are energy limited, they upload data to the UAV by adopting backscatter communication technology to reduce energy consumption. Meanwhile, the updated information usually contains a small number of information bits but requires low latency and high reliability, thus the finite blocklength regime in ultra-reliable and low-latency communication is adopted for SEs’ data transmission to the UAV. To keep the freshness of the updated information, a joint resource allocation problem including data collection time allocation, transmission power and trajectory design of the UAV is formulated as an optimization problem to minimize the average age of information (AoI) of all SEs. The formulated problem mixes discrete and continuous variables, which makes it difficult to solve. Thereby, we decompose the optimization problem into data collection time minimization subproblem and UAV trajectory design subproblem, which are solved by the successive convex approximation method, and the backtracking algorithm and the genetic algorithm, respectively. Numerical results show that the backtracking-based algorithm that can obtain the optimal trajectory of the UAV gains the minimal average AoI, and the genetic-based algorithm achieves sub-optimal average AoI with much lower computational complexity. The results also demonstrate that the average AoI of the backtracking-based algorithm and the genetic-based algorithm is reduced by up to 54% and 46% compared with the greedy-based benchmark algorithm, respectively.

An ADS-B Information-Based Collision Avoidance Methodology to UAV

A collision avoidance method that is specifically tailored for UAVs (unmanned aerial vehicles) operating in converging airspace is proposed. The method is based on ADS-B messages and it aims to detect and resolve conflicts between UAVs. The proposed method involves two main steps. First, a UAV conflict-sensing scheme is developed, which utilizes ADS-B information flow path and analyzes the message format information. Second, an unscented Kalman filter is used to predict UAV trajectories based on the acquired ADS-B information. The predicted information is then used to determine potential conflict scenarios, and different deconfliction strategies are selected accordingly. These strategies include speed regulation, direction regulation, and compound deconfliction, and are mathematically validated using the velocity obstacle method. The feasibility and effectiveness of the proposed method are evaluated through simulation, and it is concluded that the method can significantly improve the conflict resolution capability of UAV flights. This research provides a valuable contribution to the field of UAV collision avoidance, and can serve as a theoretical foundation for further advancements in this area.

Deep Reinforcement Learning Based UAV for Securing mmWave Communications

This paper focuses on the unmanned aerial vehicle (UAV) enabled millimeter (mmWave) communications from physical layer security perspective. A UAV is arranged as an aerial base station to provide ubiquitous connectivity for terrestrial users in the presence of multiple eavesdroppers. With statistical channel state information (CSI) of eavesdroppers, the beamforming vector and trajectory of UAV as well as user scheduling are jointly optimized to minimize the weighted sum of UAV flight period and secrecy outage duration. The considered problem is a combinatorial optimization problem with complicated objective function, and thus difficult to be solved by convex optimization-based methods. To this end, we formulate this problem as a Markov decision process (MDP) and develop a deep reinforcement learning (DRL) based method to optimize all variables simultaneously. Simulation results validate superiority of the proposed method over benchmarks and demonstrate its ability to obtain a compromise between secure transmission and energy efficiency.

Joint Trajectory and Resource Optimization for Covert Communication in UAV-Enabled Relaying Systems

A novel covert communication framework is proposed to enhance the privacy of the unmanned aerial vehicle (UAV)-enabled relaying systems. Specifically, the full-duplex (FD) UAV covertly relays data from each ground sensor to remote base station (BS) against a warden (Willie). Considering a noise uncertainty model, the minimum detection error probability (DEP) of Willie is first derived. Under the constraint of the derived DEP, the minimum average covert transmission rate (ACTR) is then maximized by jointly designing the sensor association, the UAV transmit power, and the UAV trajectory. A low-complexity algorithm based on the penalty successive convex approximation (P-SCA) is further invoked to achieve the locally optimal solution. Finally, numerical results show the effectiveness of the proposed framework and reveal some insights, i.e., increasing uncertainty at Willie can improve the covertness performance.

Capacity Maximization in RIS-UAV Networks: A DDQN-Based Trajectory and Phase Shift Optimization Approach

Reconfigurable Intelligent Surface (RIS) has grown rapidly due to its performance improvement for wireless networks, and the integration of unmanned aerial vehicle (UAV) and RIS has obtained widespread attention. In this paper, the downlink of non-orthogonal multiple access (NOMA) UAV networks equipped with RIS is considered. The objective is to optimize the UAV trajectory with RIS phase shift to maximize the system capacity under the UAV energy consumption constraint. By deep reinforcement learning, a capacity maximization scheme under energy consumption constraints based on double deep Q-Network (DDQN) is proposed. The joint optimization of UAV trajectory with RIS phase shift design is achieved by DDQN algorithm. From the numerical results, the proposed optimization scheme can increase the system capacity of the RIS-UAV-assisted NOMA networks.

UAV-Enabled Mobile-Edge Computing for AI Applications: Joint Model Decision, Resource Allocation, and Trajectory Optimization

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air-ground integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields such as smart homes, smart factories and smart cities to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation-intensive, latency-sensitive, and energy-consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this paper, we study mobile edge computing (MEC) for AI applications in air-ground integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation and communication resource constraints. Considering the formulated problem is non-convex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.

Joint Resource Allocation and Trajectory Optimization for Completion Time Minimization for Energy-Constrained UAV Communications

An unmanned-aerial-vehicle (UAV)-aided communication scenario is studied in this research, where a UAV with constrained on-board battery capacity communicates with multiple ground users (GUs) during the flying process. We investigate the UAV trajectory as well as the resource allocation to minimize the time consumption of the specific task. The formulated problem is non-convex with innumerable variables. To make this problem tractable, a collection of line segments are utilized to represent the real trajectory through discretization. The reformulated problem is solved by applying the block coordinate descent (BCD) algorithm. To be specific, the reformulated problem is decoupled into two subproblems, i.e., resource allocation and trajectory optimization subproblems. The subproblems are both non-convex, which are efficiently resolved with the successive convex approximation (SCA) method. Then the result is obtained with an iterative algorithm. The simulation results reveal that, in comparison to benchmark schemes, the system performance has a significant improvement.

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment.

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs' impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.

Joint resource optimization and trajectory design for energy minimization in UAV-assisted mobile-edge computing systems

This paper studies an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, where a rotary-wing UAV equipped with computing platforms is used to assist ground users (GUs) with insufficient computing resource. Each GU offloads part of the task to the UAV for computation through the uplink, and the remaining computation task is computed by itself. Then, UAV will return the results to the corresponding GU via downlink. Considering the results for each GU is not only used by itself, but may also be sent to other GUs for data fusion, we thus design two specific scenarios in this paper: (1) homologous UAV-assisted MEC scenario, i.e. UAV receives the computation task of GUs and then returns the results to the identical GUs, and (2) non-homologous UAV-assisted MEC scenario, i.e., UAV receives the computation task of source GUs and returns the results to the corresponding destination GUs. For these two scenarios, we propose an energy minimization problem by jointly optimizing task partition, the time slot length and UAV trajectory. In particular, we use the path discretization approach to convert the problem as a problem form which is finite variables. Since the problem is non-convex, we use successive convex approximation (SCA) techniques to tackle the non-convexity. Considering the initial trajectory has an important affect on the experimental result, then a specific trajectory initialization design via combining with the Pickup-and-Delivery Problem (PDP) is proposed. Numerical results proof our proposed design is superior compared with the benchmark cases.

Joint resource scheduling and trajectory design for multi-UAV-assisted uplink NOMA networks

The combination of unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) is of great significance to support massive connections in the Internet of things (IoT). This paper studies a multi-UAV-assisted communication network, where the UAVs are employed as aerial access points to receive data from IoT nodes with NOMA adopted for uplink transmission. The aim is to maximize the average sum rate of the network, with the requirements of the individual upload of each node. To handle the node clustering problem, the spectral clustering and maximum weight matching algorithm are first integrated to divide the IoT nodes. Then, the time-varying NOMA user grouping scheme is implemented accordingly. Furthermore, by considering the trajectory decomposition and successive hover-and-fly structure, the complex UAV trajectory problem is transformed into a linear programming and the optimal time-continuous trajectory is achieved. Extensive simulations validate the effectiveness of the proposed algorithms for the UAV-assisted NOMA network.

Trajectories Generation for Unmanned Aerial Vehicles Based on Obstacle Avoidance Located by a Visual Sensing System

In this work, vectorial trajectories for unmanned aerial vehicles are completed based on a new algorithm named trajectory generation based on object avoidance (TGBOA), which is presented using a UAV camera as a visual sensor to define collision-free trajectories in scenarios with randomly distributed objects. The location information of the objects is collected by the visual sensor and processed in real-time. This proposal has two advantages. First, this system improves efficiency by focusing the algorithm on object detection and drone position, thus reducing computational complexity. Second, online trajectory references are generated and updated in real-time. To define a collision-free trajectory and avoid a collision between the UAV and the detected object, a reference is generated and shown by the vector, symmetrical, and parametric equations. Such vectors are used as a reference in a PI-like controller based on the Newton–Euler mathematical model. Experimentally, the TGBOA algorithm is corroborated by developing three experiments where the F-450 quadcopter, MATLAB® 2022ª, PI-like controller, and Wi-Fi communication are applied. The TGBOA algorithm and the PI-like controller show functionality because the controller always follows the vector generated due to the obstacle avoidance.

Seamless and Energy-Efficient Maritime Coverage in Coordinated 6G Space–Air–Sea Non-Terrestrial Networks

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air–sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge. The design of an SAS-NTN for energy-efficient resource allocation is investigated in this study. The goal is to maximize system energy efficiency (EE) by collaboratively optimizing user equipment (UE) association, power control, and unmanned aerial vehicle (UAV) deployment. Given the limited payloads of UAVs, this work focuses on minimizing the total energy cost of UAVs (trajectory and transmission) while meeting EE requirements. A mixed-integer nonlinear programming problem is proposed, followed by the development of an algorithm to decompose, and solve each problem distributedly. The binary (UE association) and continuous (power, deployment) variables are separated using the Bender decomposition (BD), and then the Dinkelbach algorithm (DA) is used to convert fractional programming into an equivalent solvable form in the subproblem. A standard optimization solver is utilized to deal with the complexity of the master problem for binary variables. The alternating direction method of multipliers (ADMM) algorithm is used to solve the subproblem for the continuous variables. Our proposed algorithm provides a suboptimal solution, and simulation results demonstrate that the algorithm achieves better EE and spectral efficiency (SE) than baselines.

GA-DCTSP: An Intelligent Active Data Processing Scheme for UAV-Enabled Edge Computing

With the prosperous development of the Internet of Things (IoT), large numbers of IoT devices have been deployed in many scenarios to perceive the data of surroundings. To provide real-time service, devices prefer to transfer the data to servers for faster processing. However, in some special areas with limited communication infrastructure, devices cannot be directly connected to the Internet, so the requirements of data collection and processing are agnostic, which brings a huge challenge for real-time and dynamic data processing. We make the first attempt to tackle this challenge by proposing an Intelligent Active Data Processing Scheme (IADP) in unmanned aerial vehicle (UAV)-enabled edge computing. The main innovations of IADP are as follows: 1) when IoT devices generate or sense data, they can intelligently and proactively spread data or data information to other areas to inform UAV to collect; 2) our scheme combines active data processing with data deadline priority and artificial intelligence (AI)-based UAV trajectory optimization to achieve maximum data processing rate and minimum UAV flight cost. Specifically, genetic algorithm-deadline constraint traveling Salesman problem (GA-DCTSP) algorithm is proposed based on improved GA to achieve it; and 3) an intelligent data processing decision mechanism of optimally determining transmitting to UAV or edge server is proposed to further optimize network performance. Finally, the experimental results show that our proposed IADP increases the data processing rate by 48.77%–232.54% and reduces the UAV flight distance by 63.45%–88.36% compared with the existing schemes.

UAV Trajectory Design and Power Optimization for Terahertz Band-Integrated Sensing and Communications

Sixth generation (6G) wireless networks require very low latency and an ultra-high data rate, which have become the main challenges for future wireless communications. To effectively balance the requirements of 6G and the extreme shortage of capacity within the existing wireless networks, sensing-assisted communications in the terahertz (THz) band with unmanned aerial vehicles (UAVs) is proposed. In this scenario, the THz-UAV acts as an aerial base station to provide information on users and sensing signals and detect the THz channel to assist UAV communication. However, communication and sensing signals that use the same resources can cause interference with each other. Therefore, we research a cooperative method of co-existence between sensing and communication signals in the same frequency and time allocation to reduce the interference. We then formulate an optimization problem to minimize the total delay by jointly optimizing the UAV trajectory, frequency association, and transmission power of each user. The resulting problem is a non-convex and mixed integer optimization problem, which is challenging to solve. By resorting to the Lagrange multiplier and proximal policy optimization (PPO) method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, given the UAV location and frequency, the sub-problem of the sensing and communication transmission powers is transformed into a convex problem, which is solved by the Lagrange multiplier method. Second, in each iteration, for given sensing and communication transmission powers, we relax the discrete variable to a continuous variable and use the PPO algorithm to tackle the sub-problem of joint optimization of the UAV location and frequency. The results show that the proposed algorithm reduces the delay and improves the transmission rate when compared with the conventional greedy algorithm.

Resource allocation method for Mobility‐Aware and Multi‐UAV‐Assisted mobile edge computing systems with energy harvesting

AbstractDue to the limited coverage of base station (BS) and battery capacity of mobile users, the resource allocation strategy in multiple unmanned aerial vehicles (UAVs)‐assisted edge computing system with nonlinear energy harvesting is investigated in this paper. The cooperation between BS and multi‐UAV is considered, which can provide extensive coverage for users with mobility. Mobile users can simultaneously offload computation bits to the BS and the UAV, and mobile users harvest energy from BS and UAV. Meanwhile, the mobility of users is taken into account. Moreover, an echo state network (ESN)‐based prediction algorithm is utilized for predicting the future positions of mobile users. Therefore, the UAV can reach the predicted users' positions in advance to ensure the continuity of communication. The objective of the resource allocation strategy is to maximize the energy efficiency by jointly optimizing bandwidth allocation, computation resources, the trajectory of UAV, and transmitting power of mobile users. Then, the resource allocation problem is formulated as a mixed‐integer nonlinear programming problem. The quantum‐behaved particle swarm optimization (QPSO) algorithm is used to solve the problem. Simulation results demonstrate that the proposed strategy can achieve higher energy efficiency than other benchmark strategies. In addition, QPSO algorithm outperforms the standard particle swarm optimization algorithm and genetic algorithm in terms of energy efficiency.