Unmanned Aerial Vehicle Base Stations Research Articles

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

Content Delivery Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network for Metaverse Users

Content Delivery Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network for Metaverse Users

A stochastic geometry-based performance analysis of a UAV corridor-assisted IoT network

The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.

UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

Estimation formula of unmanned aerial vehicle base stations density for required line-of-sight probability in urban environments

Estimation formula of unmanned aerial vehicle base stations density for required line-of-sight probability in urban environments

Layerwise Quantum Deep Reinforcement Learning for Joint Optimization of UAV Trajectory and Resource Allocation

This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: (1) UAV trajectory planning, (2) user grouping, and (3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its non-convexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station while maintaining quality-of-service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV base station in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.

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.

Coverage Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network

Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.

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.

Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications

The collaborative deployment of multiple UAVs is a crucial issue in UAV-supported disaster emergency communication networks, as utilizing these UAVs as air base stations can greatly assist in restoring communication networks within disaster-stricken areas. In this paper, the problem of rapid deployment of randomly distributed UAVs in disaster scenarios is studied, and a distributed rapid deployment method for UAVs´ emergency communication network is proposed; this method can cover all target deployment points while maintaining connectivity and provide maximum area coverage for the emergency communication network. To reduce the deployment complexity, we decoupled the three-dimensional UAV deployment problem into two dimensions: vertical and horizontal. For this small-area deployment scenario, a small area UAVs deployment improved-Broyden–Fletcher–Goldfarb–Shanno (SAIBFGS) algorithm is proposed via improving the Iterative step size and search direction to solve the high computational complexity of the traditional Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In a large area deployment scenario, aiming at the problem of the premature convergence of the standard genetic algorithm (SGA), the large-area UAVs deployment elitist strategy genetic algorithm (LAESGA) is proposed through the improvement of selection, crossover, and mutation operations. The adaptation function of connectivity and coverage is solved by using SAIBFGS and LAESGA, respectively, in the horizontal dimension to obtain the optimal UAV two-dimensional deployment coordinates. Then, the transmitting power and height of the UAV base station are dynamically adjusted according to the channel characteristics and the discrete coefficients of the ground users to be rescued in different environments, which effectively improves the power consumption efficiency of the UAV base station and increases the usage time of the UAV base station, realizing the energy-saving deployment of the UAV base station. Finally, the effectiveness of the proposed method is verified via data transmission rate simulation results in different environments.

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.

Energy-Efficient Ground Base Station Antenna Array System for Wireless Back-Hauling and Two State Charging of Drone Base Stations

A ground base station (GBS) antenna array system (AAS) geometry and feed technique are proposed for 360 degree azimuth beam scan, elevation beam scan, azimuth beam-width control, elevation beam-width control, and in existing AASs these four capabilities do not coexist. Firstly, the results confirm that the number of active radio-frequency (RF) switches in the proposed AAS feed network is reduced by more than 99 %, and the AAS achieves a power consumption reduction of up to 50 % without fully connected (FC) architecture and 35 % with FC architecture compared to the existing AAS. Also, an independent GBS AAS azimuth and elevation plane beam-width control can be achieved from 65 to 0.01 degrees which makes it suitable for GBS-assisted wireless back-hauling plus wireless charging of unmanned aerial vehicle base stations (UAV-BSs), dynamic GBS sectorisation order control, and millimeter wave beam tracking. Also, a parallel concurrent UAV-BS battery charging configuration with power source swap capability is proposed to charge UAV-BS using the GBS back-haul and onboard power amplifier (PA) system as the power sources. The results show that the proposed charging configuration improves hovering time by 180.7 % compared to the existing onboard PA system configuration for battery charging. Furthermore, the proposed charging configuration can recharge the battery of the inactive state UAV-BS to increase the state of charge of two 16 Ampere hour batteries to 80 % in 180.13 minutes, and UAV-BS’s inactive state charging was not proposed in the existing onboard PA system configuration for UAV-BS’s battery charging.

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.

Optimal stratified placement of balloons and UAVs to support users’ coverage

Unmanned aerial vehicles (UAVs) base stations are a promising solution to provide better coverage to ground users. In this paper, balloons, UAVs and users are deployed to efficiently enhance users’ coverage. This paper studies three deployment problems: (i) placement of balloons to aid UAVs for wireless assistance, (ii) placement of UAVs to provide coverage to users, and (iii) a joint problem for successful deployment of balloons, UAVs, and connectivity to users. Firstly, an optimization problem is formulated to minimize the number and costs of balloons. Secondly, the goal is to minimize the number of UAVs, maximize the number of users’ connections, minimize the distance between users and UAVs, and minimize the UAVs’ deployment costs. Thirdly, a joint problem is also introduced to provide efficient wireless coverage. The problem is formulated as an integer linear program (ILP). An optimal algorithm is used to solve the optimization problem, however, the computational complexity of an optimal algorithm is high. Thus, a low-complexity greedy algorithm is also proposed. Simulation results show the efficiency of the proposed optimal and greedy algorithms.

Intelligent Drone Positioning via BIC Optimization for Maximizing LPWAN Coverage and Capacity in Suburban Amazon Environments.

This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.

Large-Scale Rate-Splitting Multiple Access in Uplink UAV Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel

Unmanned aerial vehicles (UAVs) are capable of improving the performance of next generation wireless systems. However, their communication performance is prone to both channel estimation errors and potential eavesdropping. Hence, we investigate the effective network secrecy throughput (ENST) of the uplink UAV network, in which rate-splitting multiple access (RSMA) is employed by each legitimate user for secure transmission under the scenario of massive access. To maximize the ENST, the transmission rate versus power allocation relationship is formulated as a max-min optimization problem, relying on realistic imperfect channel state information (CSI) of both the legitimate users and passive eavesdroppers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eves$</tex-math></inline-formula> ). In the model considered, each user transmits a superposition of two messages to a UAV base-stations (UAV-BS), each having different transmit power and the UAV-BS adopts a successive interference cancellation (SIC) technique to decode the received messages. Given the non-convexity of the problem, it is decoupled into a pair of sub-problems. In particular, we derive a closed form expression for the optimal rate-splitting fraction of each user. Then, given the optimal rate-splitting fraction of each user, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constrainted transmit power of each user is calculated by harnessing sequential parametric convex approximation (SPCA) programming. Our simulation results confirm that the scheme conceived significantly improves the ENST compared to both the existing orthogonal and non-orthogonal benchmarks.

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.