Unmanned Aerial Vehicle Base Stations Research Articles

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.

Energy Consumption Optimization for UAV Base Stations With Wind Compensation

In this letter, an energy-efficient algorithm for positioning of unmanned aerial vehicle-based base stations (UAV-BSs) is presented. The objective is to reduce the propulsion power consumption of UAV-BSs while not compromising the communication capacity of user equipments (UEs). As a significant step beyond state-of-the-art, we consider an effect of wind. To this end, we develop a new model of a propulsion energy consumption for the UAV-BSs reflecting an impact of wind. Furthermore, we propose a novel algorithm based on an ensemble learning optimizing the 3D trajectory of UAV-BSs over time in realistic environment with wind to reduce the propulsion energy consumption. The results show that the proposed approach reduces the propulsion energy consumption of UAV-BSs by up to 47% with only a negligible degradation in the UEs capacity compared to state-of-the-art works.

An Improved Particle Swarm Optimization Algorithm for UAV Base Station Placement

An Improved Particle Swarm Optimization Algorithm for UAV Base Station Placement

Cooperative Trajectory Design of Multiple UAV Base Stations With Heterogeneous Graph Neural Networks

Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means for tackling eruptive communication service requirements especially when terrestrial infrastructures are unavailable. Quality of service (QoS) received by ground terminals (GTs) highly depends on the spatial movement of UAV-BSs. In this paper, we investigate the cooperative trajectory design problem of multiple UAV-BSs towards fair throughput maximization of GTs. Considering the restriction of coverage and sensing, we first propose a heterogeneous-graph-based formulation of relations between GTs and UAV-BSs. Subsequently, we design a framework named graph vision and communication (GVis&Comm) to 1) let each UAV-BS efficiently manage time-varying local observations; 2) facilitate cooperation between UAV-BSs through explicit information exchange. To further reduce the overhead of over-the-air cooperation, we realize discretization of the message passing process among UAV-BSs while still enabling end-to-end training. By leveraging multi-agent reinforcement learning (MARL), UAV-BSs as agents learn a distributed trajectory design policy. Extensive numerical simulation shows that our framework on the one hand achieves remarkable efficiency in processing local observations of each UAV-BS, and on the other improves the overall network performance via close cooperation among UAV-BSs.

Co-Channel Interference Management for Heterogeneous Networks Using Deep Learning Approach

The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs.

Joint Channel Access and Power Control Optimization in Large-Scale UAV Networks: A Hierarchical Mean Field Game Approach

With the development of the new generation communications, unmanned aerial vehicle base stations (UAV-BSs) have been widely used to enhance the terrestrial communications. Because of the increasing demand for communication, the scale of the networks is constantly expanding. This paper investigates a joint channel access and power control optimization in large-scale UAV coverage networks, and proposes a hierarchical mean field game approach. Firstly, considering the quality of service (QoS) requirement and energy consumption, the cost function of UAV-BSs is designed. A joint channel access and power control optimization framework is built to improve the efficiency of resource utilization by minimizing the cost function. Then, the joint optimization problem is formulated as a hierarchical game model whose exterior is a channel access game and interior is a multiple mean fields game (MMFG). The channel access game is proved to be a potential game and have at least one Nash Equilibrium (NE). As feedback for channel access optimization, power control policy is obtained by MMFG. A near-optimal algorithm for joint optimizations is designed to achieve the NE. Besides, aiming to the networks with limited computing capability, we also propose a fast optimization approach. The original optimization problem is divided into two independent sub-problems. The channel optimization sub-problem is formulated as a local altruistic game model, then the power policy sub-problem is solved by MMFG. A distributed algorithm based on spatial adaptive play and better response is proposed. Finally, the simulation results verify the validity of the proposed algorithms. The near-optimal optimization method has a better performance while the fast optimization method has a higher speed of the convergence. Compared with the traditional approaches, both of them improve resources utilization efficiency greatly.

Study on optimization of communication network for multi-unmanned aerial vehicles

The unmanned aerial vehicles (UAV) base station is most widely used as the emergency communication in response to emergencies. However, the performance improvement is in great demand at communication network. To improve the performance between the UAVs and the users, a novel method is proposed based on k-means++ algorithm and antenna adjusting. The established simulation platform and models are credible and reliable according to the very excellent approximation capability and the strong correlation between the simulation results and the Shannon’s formula. The transmission success rate for the three conditions (using only k-means++, antenna pointing optimization and antenna downtilt angle optimization) dramatically increases with an increase in k and all reach the maximum when k is equal to 5. The fan throughput of the proposed method is almost two times than that of the other two conditions after the k value exceeds 5. Meanwhile, the edge users’ rate and average user rate of the proposed method are all markedly increased. Overall, a novel method is provided for optimizing the performance between multi-UAVs and users, and the performance communication network for multi-UAVs is optimized by using the proposed method in this paper.

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.

Towards Energy-Efficient Data Collection by Unmanned Aerial Vehicle Base Station With NOMA for Emergency Communications in IoT

For emergency communications in an internet of thing (IoT) network, a large number of gateways are distributed to gather the data traffic. Considering the practical difficulty of deploying multiple territorial base stations (TBSs) in a wide range, unmanned aerial vehicle base station (UAV-BS) can fly to a specific point and hover above there to collect data traffic from gateways. In this paper, we aim to maximize the UAV-BS energy efficiency under the constraints of total serving delay, UAV-BS flying speed, and the maximum available transmitting power of gateways, etc. Firstly, we propose a distributed gateway cluster (GC) algorithm to group gateways into multiple GCs based on the distances among gateways. Next, the UAV-BS flies and hovers above each GC, where the gateways in the GC simultaneously transmit data to the UAV-BS by non-orthogonal multiple access (NOMA). By analyzing the NOMA feature, we propose theorems optimizing the UAV-BS hovering height to minimize the transmitting power of the gateway with the maximum transmitting power among the gateways in a GC. Based on the proposed theorems, we formulate the joint optimization problem to maximize the UAV-BS energy efficiency with only the variables of UAV-BS flying speed and the serving time for each GC. The optimization problem is effectively solved by the geometric programming (GP) method. Finally, we verify the effectiveness of the proposed algorithms by extensive simulation results.

Deployment for NOMA-UAV Base Stations Based on Hybrid Sparrow Search Algorithm

Auxiliary UAV base stations are expected to be the most promising schemes to fill the resource shortage and the blind spot of coverage of the ground base stations in the future. This paper proposes a hybrid sparrow search algorithm (SSA) to optimize the three-dimensional (3D) deployment of multi-UAV base stations with the objective of maximizing the sum log-rate utility. First, the air-to-ground channel model is established using the non-orthogonal multiple access (NOMA) technology to improve the spectrum utilization by considering the complex interference forms in multi-UAV scenarios, which is more compatible with the actual applications. Then, the multi-UAV deployment problem is described as an optimization problem to maximize the sum log-rate utility of all users. Finally, a hybrid SSA is proposed to implement the optimal 3D deployment of multi-UAV base stations. Specifically, in order to overcome the local optimization defects of the traditional SSA, the opposition-based learning (OBL) strategy is utilized to modify the initial position of the sparrows to promote particle diversity, and the sine-cosine algorithm is developed to optimize the update formula of the beggar sparrows by integrating the disturbance into the update process. Consequently, a hybrid SSA with higher search accuracy, stability and robustness is formed. The simulation results demonstrate that the proposed method outperforms the traditional methods in terms of the sum log-rate utility and throughput.

Development and applications of the UAV remote sensing network based on the intelligent UAV base station

Development and applications of the UAV remote sensing network based on the intelligent UAV base station

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

Uncrewed aerial vehicle-mounted base stations (UAV-BSs), also know as drone base stations, are considered to have promising potential to tackle the limitations of ground base stations. They can provide cost-effective Internet connection to es that are out of infrastructure. They can also take over quickly as service providers when ground base stations fail in an unanticipated manner. UAV-BSs benefit from their mobile nature that enables them to change their 3D locations if the demand profile changes rapidly. In order to effectively leverage the mobility of UAV-BSs so as to maximize the performance of the network, 3D location of UAV-BSs requires continuous optimization. However, solving the optimization problem of UAVBSs is NP-hard with no deterministic solution in polynomial time. In this paper, we propose a continuous actor-critic deep reinforcement learning solution in order to solve the location optimization problem of UAV-BSs in the presence of mobile endpoints. The simulation results show that the proposed model significantly improves the network performance compared to Qlearning, deep Q-learning and conventional algorithms. While the Q-learning and deep Q-learning-based baselines reach the sum data rate of 35 Mbps and 42 Mbps respectively, our proposed ACDQL-based strategy maximizes the sum data rate of endpoints to 45 Mbps. Furthermore, the proposed ACDQLbased methodology reduces the convergence time of the UAV-BS placement optimization by 85 percent compared to the Q-learning and deep Q-learning baselines.

Performance Analysis of Unmanned Aerial Vehicle Enabled Wireless Power Transfer Considering Radio Frequency System Imperfections

A simple and energy-efficient communication system configuration is proposed for Unmanned Aerial Vehicle Energy Transmitters (UAV-ETs) for Wireless Power Transfer (WPT) applications. The results show that the proposed UAV-ET’s hardware configuration improves WPT time by 9.305%, and reduces UAV-ET’s power consumption by 7.47% compared to the UAV Base Stations (UAV-BSs). Also, in the prior works on UAV-enabled WPT the UAV-ET’s communication system configuration was not proposed, and UAV-ET’s radio frequency component’s real-world imperfections were ignored. Therefore, the work proposes a holistic energy efficiency optimization framework for UAV-ETs and hardware design parameters for qualifying the UAV-ET’s hardware components to maximize UAV-ET’s WPT time. Additionally, a three-dimensional UAV-ET placement optimization is proposed for maximizing WPT time, and results show that higher UAV-ET heights above the Wireless Energy-Receivers (WERs) decrease the WPT time by 4.75%. Besides, the results suggest that Antenna Array (AA) losses and the power amplifier’s power added efficiency variation reduce UAV-ET’s WPT time by 16.48%. Finally, a 115 g crossed-slotted waveguide AA for UAV-ET is manufactured using laser cutting, and the experiments confirm that the AA’s total loss is 0.72 dB, therefore, the AA qualifies requirements according to the proposed UAV-ET’s hardware component design optimization framework.

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.

Access Control and Deployment Design for Multi-UAV Assisted Wireless Networks

In this letter, we investigate the ground user (GU) access control and unmanned aerial vehicle base station (UAV-BS) deployment design in a multiple UAV-BSs assisted wireless network. The objective of each GU is to maximize its own throughput, and the objective of each UAV-BS is to maximize the minimum throughput of GUs served by it. Firstly, we optimize the GU access control by game theory. The access control game is proved to be an exact potential game, and we use the best response algorithm to solve the Nash equilibrium (NE). Then, we optimize the UAV-BS deployment design through convex optimization theory. The simulation results demonstrate that the proposed approach outperforms other five baselines.

Maximizing the Secrecy Energy Efficiency of the Cooperative Rate-Splitting Aided Downlink in Multi-Carrier UAV Networks

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting their downlink transmissions against an external eavesdropper ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> ). Furthermore, we consider the challenging scenario in which CRS is employed by each multi-antenna UAV-BS to protect their corresponding downlink transmissions against an external <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> . We further consider a difficult scenario in terms of security in which only imperfect channel state information of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> is available at the Tx. Accordingly, we conceive a robust secure resource allocation algorithm, which maximizes the SEE by jointly optimizing both the user association matrix and the network parameter allocation problem, including the RS precoders, time slot sharing and power allocation. Due to the non-convexity of the problem, it is decoupled into a pair of convex sub-problems. Firstly, new two-tier intra-cell optimization problems are formulated for achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> -optimal solutions by iterative block coordinate decent programming. Then, the power of each sub-channel is optimized by formulating the associated power control problem.