Unmanned Aerial Vehicles Trajectory Design Research Articles

LEO-Satellite-Assisted UAV: Joint Trajectory and Data Collection for Internet of Remote Things in 6G Aerial Access Networks

As the sixth generation (6G) network is under research, and one important issue is the aerial access network and terrestrial-space integration. The Internet of Remote Things (IoRT) sensors can access the unmanned aerial vehicles (UAVs) in the air, and low Earth orbit (LEO) satellite networks in the space help to provide lower transmission delay for delay-sensitive IoRT data. Therefore, in this article, we consider the LEO satellite-assisted UAV data collection for the IoRT sensors. Specifically, a UAV collects the data from the IoRT sensors, then two transmission modes for the collected data back to Earth: 1) the delay-tolerant data leveraging the carry-store mode of UAVs to Earth and 2) the delay-sensitive data utilizing the UAV-satellite network transmission to Earth. Considering the limited payloads of UAVs, we focus on minimizing the total energy cost (trajectory and transmission) of UAVs while satisfying the IoRT demands. Due to the intractability of direct solution, we deal with the problem using the Dantzig-Wolfe decomposition and design the column generation-based algorithms to efficiently solve the problem. Moreover, we present a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks. Finally, numerical results verify the efficiency of the proposed algorithms and the advantage of LEO satellite-assisted UAV trajectory design combined with the data transmission is also analyzed.

3D UAV Trajectory Design and Frequency Band Allocation for Energy-Efficient and Fair Communication: A Deep Reinforcement Learning Approach

Unmanned Aerial Vehicle (UAV)-assisted communication has drawn increasing attention recently. In this paper, we investigate 3D UAV trajectory design and band allocation problem considering both the UAV’s energy consumption and the fairness among the ground users (GUs). Specifically, we first formulate the energy consumption model of a quad-rotor UAV as a function of the UAV’s 3D movement. Then, based on the fairness and the total throughput, the fair throughput is defined and maximized within limited energy. We propose a deep reinforcement learning (DRL)-based algorithm, named as EEFC-TDBA (energy-efficient fair communication through trajectory design and band allocation) that chooses the state-of-the-art DRL algorithm, deep deterministic policy gradient (DDPG), as its basis. EEFC-TDBA allows the UAV to: 1) adjust the flight speed and direction so as to enhance the energy efficiency and reach the destination before the energy is exhausted; and 2) allocate frequency band to achieve fair communication service. Simulation results are provided to demonstrate that EEFC-TDBA outperforms the baseline methods in terms of the fairness, the total throughput, as well as the minimum throughput.

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

In this paper, we consider an Internet of unmanned aerial vehicles (UAVs) over cellular networks, where UAVs work as aerial users to collect various sensory data, and send the collected data to their transmission destinations over cellular links. Unlike the terrestrial users in the conventional cellular networks, different UAVs have various communication requirements due to their sensing applications, and a more flexible communication framework is in demand. To tackle this problem, we propose a UAV-to-Everything (U2X) networking, which enables the UAVs to adjust their communication modes full dimensionally according to the requirements of their sensing applications. In this article, we first introduce the concept of U2X communications, and elaborate on its three communication modes. Afterwards, we discuss the key techniques of the U2X communications, including joint sensing and transmission protocol, UAV trajectory design, and radio resource management. A reinforcement learning-based mathematical framework for U2X communications is then proposed. Finally, the extensions of the U2X communications are presented.

Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.

Non-Orthogonal Multiple Access for Air-to-Ground Communication

This paper investigates ground-aerial uplink non-orthogonal multiple access (NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and multiple ground users (GUEs) are served by ground base stations (GBSs) by utilizing the uplink NOMA protocol. The UAV is dispatched to upload specific information bits to each target GBSs. Specifically, our goal is to minimize the UAV mission completion time by jointly optimizing the UAV trajectory and UAV-GBS association order while taking into account the UAV's interference to non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite variables. To tackle this problem, we efficiently check the feasibility of the formulated problem by utilizing graph theory and topology theory. Next, we prove that the optimal UAV trajectory needs to satisfy the \emph{fly-hover-fly} structure. With this insight, we first design an efficient solution with predefined hovering locations by leveraging graph theory techniques. Furthermore, we propose an iterative UAV trajectory design by applying successive convex approximation (SCA) technique, which is guaranteed to coverage to a locally optimal solution. We demonstrate that the two proposed designs exhibit polynomial time complexity. Finally, numerical results show that: 1) the SCA based design outperforms the fly-hover-fly based design; 2) the UAV mission completion time is significantly minimized with proposed NOMA schemes compared with the orthogonal multiple access (OMA) scheme; 3) the increase of GUEs' quality of service (QoS) requirements will increase the UAV mission completion time.

3D UAV Trajectory and Communication Design for Simultaneous Uplink and Downlink Transmission

In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

Energy-Efficiency for IoT System With Cache-Enabled Fixed-Wing UAV Relay

This paper studies the energy efficiency (EE) maximization problem of fixed-wing Unmanned aerial vehicles (UAVs) enabled Internet of Things (IoT) system, where an UAV is employed as an aerial relay with enough cache capacity to amplify and forward (AF) received signals between narrow band (NB)-IoT device and eNodeB. This paper optimizes the unconstrained trajectory of the UAV to maximize the EE of UAV-enabled IoT system to achieve the green communication. Due to the objective function is a fraction and is non-convex, it's hard to solve the optimization problem. Therefore, this paper alternately optimizes trajectory in source subspace and destination subspace with the other fixed and comes up with an algorithm based on successive convex approximation (SCA) method and Dinkelbach method to obtain a local optimal solution. Numerical results show that the proposed UAV trajectory design method can obtain much bigger EE than the running track (RT) trajectory and circular trajectory. Besides, the proposed cache-enabled AF strategy can obtain much bigger EE than non cache-enabled AF strategy.

UAV-Enhanced Intelligent Offloading for Internet of Things at the Edge

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge computing (MEC) has been brought to settle the conflict between computation-intensive applications and resource-limited IoT mobile devices (IMDs). Note that the assistance of unmanned aerial vehicles (UAVs) is of great importance in providing reliable connectivity in areas with limited or no available communication infrastructure. To cope with the surging demands for Big Data processing from UAV-aided IoT applications, combining UAV-aided communication and MEC has been envisioned to be a promising paradigm, which gives rise to the so-called UAV-enhanced edge. In consideration of IMDs' limited battery capacity and UAV energy budget, in this article we study the energy reduction problem in UAV-enhanced edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory. This joint optimization problem is formulated as a mix-integer nonconvex optimization problem, and an alternative optimization algorithm based on block coordinate descent and successive convex approximation techniques is proposed as our solution. Extensive numerical results demonstrate that the overall energy consumption for accomplishing the tasks can be effectively reduced by adopting our joint optimization scheme, and the necessity of task offloading, UAV trajectory design, and bit allocation during transmission is validated.

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is non-convex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

Iterative Trajectory Optimization for Physical-Layer Secure Buffer-Aided UAV Mobile Relaying.

With the fast development of commercial unmanned aerial vehicle (UAV) technology, there are increasing research interests on UAV communications. In this work, the mobility and deployment flexibility of UAVs are exploited to form a buffer-aided relaying system assisting terrestrial communication that is blocked. Optimal UAV trajectory design of the UAV-enabled mobile relaying system with a randomly located eavesdropper is investigated from the physical-layer security perspective to improve the overall secrecy rate. Based on the mobility of the UAV relay, a wireless channel model that changes with the trajectory and is exploited for improved secrecy is established. The secrecy rate is maximized by optimizing the discretized trajectory anchor points based on the information causality and UAV mobility constraints. However, the problem is non-convex and therefore difficult to solve. To make the problem tractable, we alternatively optimize the increments of the trajectory anchor points iteratively in a two-dimensional space and decompose the problem into progressive convex approximate problems through the iterative procedure. Convergence of the proposed iterative trajectory optimization technique is proved analytically by the squeeze principle. Simulation results show that finding the optimal trajectory by iteratively updating the displacements is effective and fast converging. It is also shown by the simulation results that the distribution of the eavesdropper location influences the security performance of the system. Specifically, an eavesdropper further away from the destination is beneficial to the system’s overall secrecy rate. Furthermore, it is observed that eavesdropper being further away from the destination also results in shorter trajectories, which implies it being energy-efficient as well.

Intelligent Trajectory Design in UAV-Aided Communications With Reinforcement Learning

In this correspondence paper, we focus on a cellular network aided an unmanned aerial vehicle (UAV) that serves as an aerial base station for multiple ground users. The UAV's trajectory design is investigated to maximize the expected uplink sum rate with inaccessibility to user-side information, such as locations and transmit power as well as channel parameters. The problem is formulated as a Markov decision process and solved with model-free reinforcement learning. Due to the continuous and deterministic action space, the deterministic policy gradient (DPG) algorithm is applied for the reinforcement learning model. Experiment results show that due to the great generalizability of the reinforcement learning model, the UAV is able to intelligently track the ground users with the learned trajectory despite being unaware of the user-side information and channel parameters, even when the ground users are mobile. The performance of the learned trajectory is fairly close to that of the optimized trajectory derived through conventional optimization problem solving with such information explicitly known. Moreover, we also show that the DPG algorithm converges efficiently with acceptable training time.

Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks With Sense-and-Send Protocol

Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks. The performance of a UAV is determined by both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for the UAV to determine its trajectory, since it works in a dynamic environment, where other UAVs determine their trajectories dynamically and compete for the limited spectrum resources in the same time. To tackle this challenge, we adopt the reinforcement learning to solve the UAV trajectory design problem in a decentralized manner. To coordinate multiple UAVs performing real-time sensing tasks, we first propose a sense-and-send protocol, and analyze the probability for successful valid data transmission using nested Markov chains. Then, we propose an enhanced multi-UAV Q-learning algorithm to solve the decentralized UAV trajectory design problem. Simulation results show that the proposed algorithm converges faster and achieves higher utilities for the UAVs, compared to traditional singleand multi-agent Q-learning algorithms.

Learning to Communicate in UAV-Aided Wireless Networks: Map-Based Approaches

We consider a scenario where an unmanned aerial vehicle (UAV)-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with: 1) optimal channel parameters learning and 2) optimal data throughput as key objectives, respectively. While the problem of throughput optimized trajectories has been addressed in prior works, the formulation of an optimized trajectory to efficiently discover the propagation parameters has not yet been addressed. When it comes to the communication phase, the advantage of this paper comes from the exploitation of a 3-D city map. Unfortunately, the communication trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent Internet of Things data harvesting.

Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective

Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main paradigms in cellular UAV communications, i.e., cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location, during which it needs to maintain reliable communication with the cellular network by associating with one GBS at each time instant. We aim to minimize the UAV's mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target. To tackle this challenging non-convex problem, we first propose a graph connectivity based method to verify its feasibility. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging graph theory and convex optimization techniques. The proposed methods are analytically shown to be capable of achieving a flexible trade-off between complexity and performance, and yielding a solution that is arbitrarily close to the optimal solution in polynomial time. Finally, we make concluding remarks and point out some promising directions for future work.

Trajectories and Resource Management of Flying Base Stations for C-V2X.

In a vehicular scenario where the penetration of cars equipped with wireless communication devices is far from 100% and application requirements tend to be challenging for a cellular network not specifically planned for it, the use of unmanned aerial vehicles (UAVs), carrying mobile base stations, becomes an interesting option. In this article, we consider a cellular-vehicle-to-anything (C-V2X) application and we propose the integration of an aerial and a terrestrial component of the network, to fill the potential unavailability of short-range connections among vehicles and address unpredictable traffic distribution in space and time. In particular, we envision a UAV with C-V2X equipment providing service for the extended sensing application, and we propose a UAV trajectory design accounting for the radio resource (RR) assignment. The system is tested considering a realistic scenario by varying the RRs availability and the number of active vehicles. Simulations show the results in terms of gain in throughput and percentage of served users, with respect to the case in which the UAV is not present.

An Efficient Solution for Joint Power and Trajectory Optimization in UAV-Enabled Wireless Network

The use of unmanned aerial vehicles (UAV) as aerial communication platforms has gained significant attention due to their favorable air-to-ground channels, on-demand deployment, and highly controllable mobility. In this paper, we aim at developing an efficient algorithm for the joint transmit power and UAV trajectory design via maximizing the minimum average throughput in a UAV-enabled network. The conventional method to tackle this problem is to optimize transmit power or trajectory in an alternative manner, which generally incurs high computational complexities. Moreover, how to find a favorable initial point is a challenging task. To this end, we impose a novel constraint by assuming that the UAV will not communicate until it approaches the closest position to a user, thus transforming the initial problem into an approximate one with reduced variables. Based on this result, we further propose two low-complexity algorithms by exploiting the alternating directional method of multipliers (ADMM), whose updating step is performed in closed-form solutions. In the first algorithm, both UAV trajectory and transmit power are determined simultaneously. We also propose a second algorithm which iteratively optimizes transmit power and trajectory. The simulation results show that by using the proposed ADMM-based algorithms, one can achieve higher performance than state-of-the-art methods with reduced computation time.

Joint Maneuver and Beamwidth Optimization for UAV-Enabled Multicasting

Unmanned aerial vehicles (UAVs)-assisted communications have been an essential complement of conventional wireless networks. In this paper, we consider a UAV-enabled multicasting system, where a UAV with a directional antenna of adjustable beamwidth is employed to disseminate a common file to a group of ground users. By minimizing the mission completion time, we investigate how beamwidth control would affect the UAV's three-dimensional (3D) location/trajectory. First, we consider the quasi-stationary UAV scenario, where the UAV is deployed at a static location. In this case, we jointly optimize the 3D UAV location and antenna beamwidth under the practical constraints on the UAV's altitude and beamwidth, while ensuring that all users are covered by the main lobe of the UAV antenna. Although this problem is nonconvex, its global optimality can be obtained by using a two-step algorithm, where semi-closed solutions of the 3D location and beamwidth are derived. Next, in the mobile UAV scenario, a joint 3D UAV trajectory and beamwidth design is proposed, additionally constrained by the horizontal and vertical speed. To tackle this nonconvex problem, we develop an iterative optimization algorithm based on successive convex approximation techniques, which updates the 3D trajectory and beamwidth simultaneously in each iteration. Numerical results demonstrate the effectiveness of the proposed design as compared to benchmark schemes.

Energy Efficient Estimation in Wireless Sensor Network With Unmanned Aerial Vehicle

Distributed estimation is a typical application of wireless sensor network (WSN), where a set of sensor nodes (SNs) collaboratively estimate some parameters of interest from noisy measurements. Recently, unmanned aerial vehicle (UAV) enabled WSN has attracted significant interest since the UAV can collect data energy-efficiently due to its high mobility. In this paper, we consider the joint optimization of UAV trajectory design and SNs' transmission bits allocation for estimating an unknown parameter in UAV-enabled WSN, and the objective is to minimize the total energy consumption of all SNs under the constraint that the mean square error (MSE) of estimation is below a target threshold. The joint optimization problem is formulated with mixed-integer non-convex programming, which is difficult to solve in general. As such, an efficient iterative algorithm is proposed to solve it by applying the block coordinate descent and successive convex optimization techniques. A low-complexity and systematic initialization scheme is also proposed for the trajectory design and transmission bits allocation based on the trade-off structure on the number of visited SNs for estimation. The extensive simulation results are provided to demonstrate the significant performance gains in terms of total energy consumption of all SNs as compared with other benchmark schemes.

UAV-Enabled Radio Access Network: Multi-Mode Communication and Trajectory Design

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled radio access network (RAN) with the UAV acting as an aerial platform to communicate with a set of ground users (GUs) in a variety of modes of practical interest, including data collection in the uplink, data transmission in the downlink, and data relaying between GUs involving both the uplink and downlink. Under this general framework, two UAV operation scenarios are considered: periodic operation, where the UAV serves the GUs in a periodic manner by following a certain trajectory repeatedly, and one-time operation where the UAV serves the GUs with one single fly and then leaves for another mission. In each scenario, we aim to minimize the UAV periodic flight duration or mission completion time, while satisfying the target rate requirement of each GU via a joint UAV trajectory and communication resource allocation design approach. Iterative algorithms are proposed to find efficient locally optimal solutions by utilizing successive convex optimization and block coordinate descent techniques. Moreover, as the quality of the solutions obtained by the proposed algorithms critically depends on the initial UAV trajectory adopted, we propose new methods to design the initial trajectories for both operation scenarios by leveraging the existing results for solving the classic Traveling Salesman Problem (TSP) and Pickup-and-Delivery Problem (PDP). Numerical results show that the proposed trajectory initialization designs lead to significant performance gains compared to the benchmark initialization based on circular trajectory.

Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations (BSs) are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAVs' trajectory and power control. The formulated problem is a mixed integer non-convex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the non-convex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge to at least a locally optimal solution. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.