Unmanned Aerial Vehicle Base Research Articles

HiMAQ: Hierarchical multi-agent Q-learning-based throughput and fairness improvement for UAV-Aided IoT networks

Recently, various types of Internet of Things (IoT) services have become wide spread, and new types of IoT devices are emerging. However, the significant number of high-rise buildings in urban environments renders it difficult to provide seamless and high-speed network connectivity consistently to these IoT devices via existing terrestrial IoT networks. Therefore, we herein consider unmanned aerial vehicle (UAV)-aided IoT networks, in which a ground base station (GBS) and a UAV base station (UBS) coexist. In this paper, a hierarchical multi-agent Q-learning (HiMAQ) framework is proposed to maximize the system throughput while minimizing device outage in UAV-aided IoT networks. The proposed HiMAQ adopts distributed multi-agent inner-loop reinforcement learning (RL) for determining the optimal transmission power of UBSs and GBSs, and distributed outer-loop RL to determine the optimal UBS deployment. This hierarchical RL architecture can reduce the computational complexity of the proposed RL approach compared with the centralized RL approach. The performance of HiMAQ in various network conditions is demonstrated considering the mobility of IoT devices and even/uneven spatial traffic distributions. Simulation results show that the proposed HiMAQ can maximize the system throughput and minimize the number of outage devices compared with various benchmark algorithms.

Dynamic redeployment of UAV base stations in large-scale and unreliable environments

The deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BSs) has emerged as a promising solution to enhance communication services provided to ground users. However, deploying UAV-BSs faces challenges including the cooperation of multiple UAVs, dynamic user distribution, low-reliability issues of UAVs, and efficient redeployment in large environments. Existing literature addresses some of these challenges but lacks a comprehensive approach. In this paper, we investigate all these challenges and propose a novel deployment framework with the objective of maximizing the quality of communication service by dynamically deploying UAV-BSs. The proposed framework employs a decentralized approach, allowing UAV-BSs to locally adjust their locations and rapidly respond to changes, thus ensuring stable and efficient service supply to ground users. In addition, we implement multiple simulation experiments to evaluate the performance of the proposed framework in solving the UAV-BSs redeployment problem in large and dynamic environments. The results demonstrate its ability to effectively improve the quality of communication services.

Adaptive Beam Alignment and Optimization for IRS-Aided High-Speed UAV Communications

In the unmanned aerial vehicle (UAV) enabled wireless communication system, the rapid channel change caused by high-speed UAV may cause the beam misalignment problem. It is a challenge to fast establish and maintain the communications link between UAV and ground base stations. To address this challenge, an IRS-aided high-speed UAV adaptive beam alignment (IRS-UAV-ABA) scheme utilizing double-layer long short-term memory (DL-LSTM) spatial angle prediction model is proposed to improve the effectiveness and reliability of the UAV communication. Firstly, DL-LSTM enabled spatial angle prediction is modeled to facilitate the calculation of the beam coverage adapted to the UAV real-time flight trajectory. Secondly, the non-convex optimization problem for maximizing the received signal-to-noise ratio of moving UAV is formulated to obtain the suboptimal solutions of the BS’ beamforming vector and the IRS’ phase shift matrix. Finally, with the aforementioned beamforming vectors, the phase shift matrix and the adaptive beam coverage, the adaptive dual-beams alignment from BS to UAV and from IRS to UAV can be achieved by the proposed DL-LSTM-enabled IRS-UAV-ABA algorithm. Simulation results verify that the proposed scheme not only can effectively avoid the beam misalignment caused by the mobility and perturbation of UAV, but also has superior spectrum efficiency and stable beamforming gain.

Optimal Trajectories of a UAV Base Station Using Hamilton-Jacobi Equations

We consider the problem of optimizing the trajectory of an Unmanned Aerial Vehicle (UAV). Assuming a traffic intensity map of users to be served, the UAV must travel from a given initial location to a final position within a given duration and serves the traffic on its way. The problem consists in finding the optimal trajectory that minimizes a certain cost depending on the velocity and on the amount of served traffic. We formulate the problem using the framework of Lagrangian mechanics. We derive closed-form formulas for the optimal trajectory when the traffic intensity is quadratic (single-phase) using Hamilton-Jacobi equations. When the traffic intensity is bi-phase, i.e. made of two quadratics, we provide necessary conditions of optimality that allow us to propose a gradient-based algorithm and a new algorithm based on the linear control properties of the quadratic model. These two solutions are of very low complexity because they rely on fast convergence numerical schemes and closed form formulas. These two approaches return a trajectory satisfying the necessary conditions of optimality. At last, we propose a data processing procedure based on a modified K-means algorithm to derive a bi-phase model and an optimal trajectory simulation from real traffic data.

Time-Efficient Joint UAV-BS Deployment and User Association Based on Machine Learning

In this paper, a time-efficient mechanism is proposed to solve the joint unmanned aerial vehicle (UAV) base station deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput and reducing the time of on-line computations. In this work, two relevant sub-problems are decoupled from the joint UDUA problem: the first one is denoted as the user association sub-problem, for certain UAV base station (UAV-BS) positions, this sub-problem is settled for finding the strategy which matches aerial and ground nodes optimally. The second sub-problem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association sub-problem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn-Munkres algorithm is used to solve the first sub-problem as an equivalent bipartite matching problem. For the UAV-BS deployment sub-problem, when the two user distributions own a high similarity, we theoretically prove that little performance decline will be introduced when the new user distribution’s optimal strategy is compared with choosing the optimal UAV-BS deployment strategy of stored user distributions. Based on our mathematical analyses, the similarity level between user distributions is well defined and becomes the key to solve the second sub-problem. According to experimental findings, the proposed UDUA mechanism, when compared to benchmark approaches, can provide near-optimum system performance with regard to average downlink total transmission throughput and failure rate with significantly decreased computing time.

Coverage and throughput analysis of an energy efficient UAV base station positioning scheme

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized.

Positioning Optimization of UAV (Drones) Base Station in Communication Networks

Unmanned aerial vehicles (UAV) and cellular networks are growing closer to being integrated in the realm of wireless communications, which will improve service quality even further. In this study, we investigate a wireless communication system in which two types of base stations—in the air and on the ground—serve separate groups of users. We analyze the effect of the aerial base station (ABS) height and transmit power on the system's downlink and uplink data rates while accounting for the reciprocal interference between the Aerial and terrestrial communication lines. The findings demonstrate that in many cases the best ABS altitude and transmit Power are either the highest or lowest values attainable. The distance between the ABS, the ABS user (AU), and the terrestrial base station user, among other factors, may affect how well they all communicate (TU). In this article we will discuss the following topics: unmanned aerial vehicle (UAV), terrestrial base station (BTS), transmit power optimization (TPO), interference (I), downlink (DL), and uplink (UL).

Cooperative hybrid beamforming design in Multi‐UAV MmWave MIMO systems

AbstractDue to the limitation of mobility and cost, it is inappropriate to utilize ground base station (GBS) to provide service in emergency scenarios or harsh environments. Unmanned aerial vehicle (UAV) is considered as a feasible way to solve the problem. Millimetre wave communication and UAV can be used together to offer wireless communications flexibly and rapidly for target area. However, fuselage jittering and energy supply would affect the quality and reliability of UAV communication seriously. To overcome the weakness of UAV, the authors prefer to use multiple UAVs to provide wireless communication service simultaneously. This paper investigates the design of hybrid beamforming in the scenarios including multiple UAVs and one end user. Cooperation strategy and alternating optimization method are applied in this design. The distributed UAV base stations are regarded as an entirety and the reference beamforming scheme obtained. Then, the beamforming problem for each UAV is formulated. Moreover, residual error minimization (REM) algorithm is proposed to find the solution of hybrid beamformers at UAV side. Finally, the beamformer of the receiver is optimized with the assist of effective channel, which includes the gain of both channel and beamformers of the transmitters. Simulation results show that the proposed algorithm can achieve near‐optimal spectral efficiency in different scenarios.

Joint Optimization of Spectrum Resource Management and Position Placement for UAV Base Station Networks

The unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.

Deep Learning-Based Autonomous UAV-BSs for NGWNs: Overview and a Novel Architecture

To address the ever-growing connectivity demand in communications, the adoption of ingenious solutions, such as utilization of unmanned aerial vehicles (UAVs) as mobile base stations, is imperative. In general, the location of a UAV base station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and are hard to run on UAVs due to energy consumption and time constraints. In this article, we overview the UAV-BS trajectory optimization problem for next generation wireless networks and show that a convolutional neural network (CNN) model can be trained to infer the location of a UAV-BS in real time. To this end, we create a framework to determine the UAV-BS locations considering the deployment of mobile users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is, indeed, capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming reinforcement learning-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues.

Path Planning Research of a UAV Base Station Searching for Disaster Victims' Location Information Based on Deep Reinforcement Learning.

Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems.

This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV. Since the problem is non-convex, an alternating optimization (AO) algorithm is proposed to solve it. Specifically, the TPC matrix and AN covariance matrix are derived by the Lagrange dual method. The alternating direction method of multipliers (ADMM), majorization-minimization (MM), and Riemannian manifold gradient (RCG) algorithms are presented, respectively, to solve the IRS phase shift matrix, and then the performance of the three algorithms is compared. Based on the proportional integral (PI) control theory, a secrecy rate gradient (SRG) algorithm is proposed to iteratively search for the UAV position by following the direction of the secrecy rate gradient. The theoretic analysis and simulation results show that our proposed AO algorithm has a good convergence performance and can increase the SR by 40.5% compared with the method without IRS assistance.

Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks

Multi unmanned aerial vehicle (UAV) network is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3-D placement of all UAV base stations (BSs) such that the formed multi-UAV network: 1) utilizes a minimum number of UAVs while ensuring—2) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial BS; and 3) wireless coverage to all ground users in the area of operation. This joint backhaul-and-coverage-aware drone deployment (BoaRD) problem is largely unaddressed in the literature and, thus, is the focus of this article. We first formulate the BoaRD problem as integer linear programming (ILP). However, the problem is NP-hard and, therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches—Backhaul-aware Greedy and random algorithm, respectively, by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground-user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.

Trajectory Planning of Aerial Robotic Manipulator Using Hybrid Particle Swarm Optimization

The trajectory planning of an aerial robotic manipulator system is studied using Hybrid Particle Swarm Optimization (HPSO). The aerial robotic manipulator is composed of an unmanned aerial vehicle (UAV) base and a robotic manipulator. The robotic manipulator is dynamically singular. In addition, strong coupling exists between the UAV base and the robotic manipulator. To overcome the problems, the trajectory planning is studied in the join space using HPSO. HPSO combines superiorities of PSO and GA (Genetic Algorithm), prohibiting particles from becoming trapped in a local minimum. In addition, the control parameters are self-adaptive and contribute to fast searching for the global optimum. The trajectory planning problem is converted into a parameter optimization problem. Each joint trajectory is parameterized with a Bézier curve. The HPSO is implemented to optimize joint trajectories, satisfying specific objectives and imposed constraints. Numerical simulations are also carried out to validate the effectiveness of the proposed method.

Intelligent network planning tool for location optimization of unmanned aerial vehicle base stations using geographical images

Nowadays, automated and intelligent solutions are required by different services and applications to satisfy current industrial necessities. Internet of things/robotic devices have been found useful in the collection and processing of data for pattern recognition, analyzing and anticipating incident information, optimization, and provision of better and timely decision-making to enable network providers to measure the quality of services, thereby ensuring efficiency. Telecommunication systems need reliable and fast internet networks for connected devices and other business activities; thus, technologies are being explored to determine efficient ways of planning and installing base stations. Recently, possibilities of deploying unmanned aerial vehicles (UAVs) as base stations had been investigated. Although successes have been recorded, not much work has been done on network planning tools for location optimization of UAV base stations (UAV-BS) in 5G and beyond networks to the best of our knowledge. In this paper, an intelligent network planning tool (iNPT) for location optimization of UAV-BS has been developed with different channel models and network planning parameters. The developed tool has resources to suggest a better positioning of the UAV-BS using the simulated annealing meta-heuristic to optimize their locations. The tool also includes a battery management interface that can calculate the battery dissipation with reference to the received and transmitted power of the UAV-BS and display the estimated time of flight for the UAV-BS. Geographical information, such as the height of a specific region, obtained from a map is considered in the simulations. The results obtained using the COST231-Hata, Hata path loss, log distance, and two-ray ground reflection models have been presented in this paper. Based on the experimental results, the developed simulator can be useful in network planning tasks to improve coverage areas using UAV-BSs. In addition, the proposed tool has a friendly interface that provides geographical images, and the BSs and UAVs optimized locations.

Energy Efficient Task Caching and Offloading in UAV-Enabled Crowd Management

Unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) networks provide ubiquitous communication and computing capacity for mobile users compared with terrestrial networks. In crowd management, the UAV base station(UAV-BS) collect computation tasks from the Internet of Things (IoT) devices and process tasks with terrestrial MEC networks cooperatively. However, energy efficiency(EE) and user mobility are the bottlenecks of UAV performance. Therefore, it is crucial to maximizing the energy efficiency(EE) of UAVs. In this paper, we propose an energy-efficient UAV-enabled MEC network composed of IoT devices, the UAV-BS, edge cloud, and the data center, and propose a Green-UAV-CoCaCo algorithm to jointly optimize communications, caching, and computation for EE of UAV. Specifically, we design a UAV trajectory model based on a greedy algorithm to predict the user’s coordinates and choose the proper edge server for task offloading. Then, the UAV-CoCaCo algorithm is proposed to maximize the EE of the task caching and offloading. Simulation results demonstrate the effectiveness of the proposed algorithm.

Performance Investigation of WDM based hybrid RF-FSO Link with Unmanned Aerial Vehicles based Optical Relays

The paper presents the design of wavelength division multiplexing (WDM) based hybrid radio frequency-free space optical communication (RF-FSOC) system for high data rate transmission in the scenarios where the direct line-of-sight (LOS) view between the end-to-end communication nodes is unavailable. As such, a simulation model of unmanned aerial vehicle (UAV) based parallel relaying mechanism incorporating amplify and reflection (AR) technique is presented for establishing a point-to-point FSOC link between UAV and ground base station. Furthermore, the power spectral characteristics of the WDM optical signals modulated by 8-PAM signalling scheme at the transmitter node of the proposed model is simulated and analysed in the Optisystem 16. The simulation results illustrate that the overall link length of the proposed UAV based hybrid system with 1 Gbps data rate and transmitted beam divergence angle of 10 mrad extends upto a maximum range of 200 m in light fog condition.

Enhancing Reliability and Security of UAV-Enabled NOMA Communications With Power Allocation and Aerial Jamming

In view of the scarce spectrum resources and inconvenient deployment in wireless communications, this work focuses on an unmanned aerial vehicle (UAV) enabled non-orthogonal multiple access (NOMA) system where a UAV deployed as a base station serves two users by NOMA. A joint power allocation and aerial jamming (PAAJ) scheme is proposed to achieve reliable and secure communications for the system in the presence of a malicious eavesdropper. To be specific, dynamic power allocation is adopted to ensure the reliability of the system and another friendly UAV jammer is introduced to improve the security of the system. The closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) are derived to evaluate the performance of the security-required user in the practical condition of probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) air-to-ground (A2G) channels. Moreover, the asymptotic EST is analyzed to obtain further insights. Numerical results show the validity of theoretical derivations and the superiority of the proposed PAAJ scheme over benchmark schemes. The EST of the UAV-enabled NOMA communication system outperforms the conventional terrestrial NOMA system at the low transmitting power from the UAV base station. There exists an optimal height of the UAV base station to maximize the EST, which provides a useful framework for designing UAV-enabled communication systems with heterogeneous service requirements.

Sliding-Window-Based RNC Scheme in UAV Multicasting: Performance Analysis and Network Optimization

Unmanned aerial vehicles (UAVs)-enabled multicasting network has attracted significant attention in recent years. However, there are still some disadvantages of existing multicasting schemes used in these systems, such as low transmission efficiency and high feedback overhead. Accordingly, we propose a sliding coding window (SCW)-based random network coding (SCWRNC) scheme for a UAV multicasting network where one UAV base station is dispatched to the multicast data stream to multiple user equipments (UEs). The proposed scheme includes an SCW scheduling original packets for encoding, a lower triangular coding structure enabling UEs to decode out information even without receiving a full set of coded packets, and a feedback-compete mechanism requiring only one UE to send feedback information. The packet scheduling process is described as a five-tuple Markov decision process. Then, we give a theoretical analysis of the proposed scheme, based on which the sliding steps of SCW and the UAV hovering location are jointly optimized to maximize the system throughput. The optimal sliding steps are obtained by applying the Greedy scheduling technique, while the UAV optimal position is obtained by minimizing the maximum outage probability of all UEs. Furthermore, we also propose a flexible feedback mechanism, which enables more than one UE to send feedback for systems with sufficient resources and a “F-SCWRNC” scheme for systems where no UE is allowed to send feedback. Numerical results show that both the proposed SCWRNC scheme and F-SCWRNC scheme could achieve significant throughput gain over the existing ones.

On the Communication Performance of Airborne Distributed Coherent Radar

Compared with a monostatic radar, airborne distributed coherent radar (ADCR) has been widely applied thanks to its flexibility, greater degree of freedom, stronger detection, and anti-jamming ability. Unlike distributed ground-based radar, the precondition for ADCR to perform tasks is maintenance of stable wireless communication links among the unmanned aerial vehicles (UAVs). Therefore, the communication channel modeling of ADCR is very important. This paper mainly analyzes the performance of a communication system composed of radar UAVs, communication UAV (relay), and ground base station. The probability density function (PDF) and outage probability (OP) of signal-to-noise ratio (SNR) at the ground terminal are derived analytically in the cases of transmission power error, UAV position error, and multi-path fading. Numerical simulation shows the validity of the derived results.