Unmanned Aerial Vehicle Base Stations Research Articles

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.

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.

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.

Analytical Evaluation of Power-Amplifier-Based Charging Methodology and Energy Efficiency Optimization Framework for Aerial Base Stations

In this article, a radio-frequency (RF) power-amplifier (PA) system configuration for the on-board energy conversion is proposed for unmanned aerial vehicle base station’s (UAV-BS’s) battery charging. First, the PA system utilizes the internal RF source for energy conversion so that perpetual battery charging is possible without external power sources. Therefore, a continuous battery charging of the UAV-BS is possible while providing wireless network coverage by hovering over the target location. In addition, the proposed PA system offers a higher RF power control resolution than the existing PA works, which is desirable for precise power control of the UAV-BS. The objective of simultaneous RF power control and battery charging by the PA system is achieved by activating one Wilkinson power divider (WPD) with a specific power splitting ratio in the power divider bank. The simulations show that the dynamic activation of ten distinct unequal split WPDs of the proposed PA system improves the RF power control resolution by $157$ and $396{\,}\%$ compared to the existing PAs. Additionally, the simulations show that the PA’s power added efficiency fluctuation is reduced by $54$ and $82{\,}\%$ compared to the existing PAs. In addition, it will be shown that the proposed on-board battery charging increases the hovering time by $7.4{\,}\%$. Furthermore, an energy-efficiency optimization framework for UAV-BS’s is proposed considering real-world communication system’s hardware imperfections, and the proposed result suggests that UAV-BS’s flight time may decrease by $50{\,}\%$ depending upon the type of UAV-BS RF components.

Improving inadequate coverage of an unmanned aerial vehicle base station: mobility strategies and delay-tolerant service requests

Improving inadequate coverage of an unmanned aerial vehicle base station: mobility strategies and delay-tolerant service requests

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.

Deployment of Heterogeneous UAV Base Stations for Optimal Quality of Coverage

This article studies the quality of coverage of deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) after disasters or during some occasional events. In particular, we focus on the problem of minimizing the average UAV-user distance, while maintaining connectivity between the UAV-BSs and some nearby stationary base stations (SBSs). The UAV-BSs can be deployed at different altitudes, and their transmission powers may also be different. We first propose a decentralized deployment algorithm for a Line-of-Sight (LoS) scenario. This algorithm allows UAV-BSs to determine their movements based on only local information. So, it is applicable in a large scale. The local optimality and the convergence of the algorithm are proved. Moreover, we discuss how to use the algorithm in Non-LoS (NLoS) scenarios. Specifically, during its movement, each UAV-BS needs to verify the connectivity requirement as well as if a future movement will lose any already covered users. This extension guarantees that the average UAV-user distance keeps reducing during the movements of UAV-BSs. Computer simulations and comparisons with a benchmark method confirm the effectiveness of the proposed algorithms in terms of the quality of coverage.

Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks.

Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.

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.

An Energy-Efficient Aerial Backhaul System With Reconfigurable Intelligent Surface

In this paper, we propose a novel wireless architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). By installing RIS on the aerial platform, rich line-of-sight and full-area coverage can be achieved, thereby, overcoming the limitations of the conventional terrestrial RIS. We consider a scenario where a sudden increase in traffic in an urban area triggers authorities to rapidly deploy unmanned-aerial vehicle base stations (UAV-BSs) to serve the ground users. In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS so that it successfully reaches the UAV-BSs. We jointly optimize the placement and array-partition strategies of aerial-RIS and the phases of RIS elements, which leads to an increase in energy-efficiency of every UAV-BS. We show that the complexity of our algorithm can be bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

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.

Multi-UAV Aided Millimeter-Wave Networks: Positioning, Clustering, and Beamforming

In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.

Finite Point Processes in a Truncated Octahedron-Based 3D UAV Network

In this paper, stochastic geometry tools are utilized to analyze the performance of a wireless network with Unmanned Aerial Vehicles (UAVs). The evaluation is based on the concept of a truncated octahedron that is the fundamental three-dimensional (3D) shape that models the basic cell in an entirely aerial cellular structure with UAV nodes only. The spatial locations of the aerial users, are modeled as a 3D Binomial Point Process (BPP). Subsequently, stochastic geometry analysis in a reference truncated octahedron located at the origin is performed and exact expressions for the distances between aerial users, are derived. To this end, expressions for evaluating the volume of the regions of the 3D cell in which an aerial user may fall, are derived. Then, by defining the signal-to-interference-plus-noise (SINR) ratio at the reference UAV-base station (UAV-BS) receiver, a performance analysis in terms of coverage probability is conducted under two interference scenarios, i.e., a pure intra-cell interference and an intra- plus inter-cell interference case. As intermediate step in the coverage probability analysis, expressions for relevant distance distributions and the Laplace transform of the aggregate interference power distribution are derived. For the sake of completeness, coverage probability analysis under intra-cell interference is also performed and compared to a 3D cell modeled as a sphere. The analytical expressions are also compared to simulation results to validate the accuracy of the proposed framework and reveal valuable insights.

Unmanned Aerial Vehicle Base Station Assisted Licensed Shared Access

A major issue in deploying aerial base stations is the strain it places on the already scarce spectrum and the associated required re-design of the radio resource allocations. To solve this problem, we propose a Licensed Shared Access- (LSA-) based aerial-terrestrial system. The proposed scheme is an integration of unmanned aerial vehicle base station (UAV-BS), as a licensee system, utilising the radio spectrum allocated to other wireless systems- the incumbent system, with the ground base-station operating at the traditional frequency band for cellular systems. However, a major challenge in using LSA for D-BSs is the spectrum unavailability while the incumbent system is active. In conventional terrestrial networks, this leads to service unavailability in a wide geographical area. To tackle this, we define a dynamic UAV-BS positioning scheme that takes into account the presence or otherwise of the incumbent on the spectrum (incumbent Busy and, Idle mode) while taking into consideration the spatial capacity demand in the licensee network. In the busy incumbent mode, the problem of the UAV-BS positioning is tantamount to a goal programming problem with the first level priority goal of fulfilling the incumbent’s interference threshold constraint. Simulation result show that in addition to, the proposed (LSA-) based aerial-terrestrial scheme, solving the challenge posed by scarcity of spectrum in aerial base station deployment, a significant improvement in the system efficiency is obtained. It is seen that the proposed dynamic UAV-BS positioning scheme achieved the desired capacity requirement of the licensee network, while ensuring the interference threshold is not exceeded, at a relatively smaller distance of 1000 m to the boundary of the incumbent’s system transmission coverage as opposed to a larger distance of about 19 Km.

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.

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.