Unmanned Aerial Vehicle Users Research Articles

A dynamic analysis of UAV -based IoT networks for efficient communication using falcon optimization approach

Unmanned Aerial Vehicles (UAVs) play a pivotal role in Internet of Things (IoT) applications by sensing data, conducting continuous surveillance, and maintaining connectivity among users. However, their high mobility and continuous surveillance for data transmission result in high energy consumption, possibly resulting in lesser network performance, including link failure, node depletion, data loss, and high delays. To address these challenges, a novel scheme termed as 'On-Demand Cross-Layered Falcon Optimization Approach (OCLFO)' has been proposed to enhance connectivity and optimize network performance in UAV-based IoT systems. The on- demand Cross-Layer Optimization (CLO) scheme facilitates data transmission across each layer of the OSI model. CLO is applied to the design of UAVs with the integration of on-demand, based on the queuing size. Falcon Optimization Approach (FOA) has been integrated with the CLO to overcome the energy consumption drawback, and provide successful data transmission between UAV users with the help of fitness value. The fitness value depends maximum on the energy and minimum on the queuing size, and this provides the energy efficient routing path for an efficient data transmission. The proposed OCLFO aims to optimize energy consumption while ensuring successful and expedited data transmission among UAV users. Simulations have been conducted using a network simulator to analyse Quality of Service (QoS) parameters, considering high mobility scenarios by varying energy levels such as 100 J and 200 J. Compared to the existing optimization algorithms, the proposed OCLFO achieves high delivery ratio, high throughput, less delay, lesser energy consumption and enhanced link efficiency.

Unlicensed Spectrum Access and Performance Analysis for NR-U/WiGig Coexistence in UAV Communication Systems

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.

Distance-Enhanced Hybrid Hierarchical Modulation and QAM Modulation Schemes for UAV Terahertz Communications

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations require substantial data and video transmission, demanding significant spectral resources. The ultra-broad bandwidth of 0.1–10 THz in the Terahertz (THz) frequency range is essential for future UAV-based wireless communications. However, the available bandwidth in the THz frequency spectrum varies with transmission distance. To enhance spectral efficiency over this variable bandwidth, we propose using hierarchical modulation (HM) in the overlapped spectrum and traditional quadrature amplitude modulation (QAM) in the non-overlapped spectrum for closer users. Furthermore, we analyze the single-user case and utilize the block-coordinated descent (BCD) method to jointly optimize the modulation order, subcarrier bandwidth, and sub-band power to improve the system sum rate. Finally, considering the mobility and randomness of UAV users, we design a modulation switching rule to dynamically adjust to changes in distance as users move, thereby enhancing data rates. Simulation results demonstrate superior performance in data rate and design complexity compared to existing methods such as hierarchical bandwidth modulation (HBM) and HM schemes.

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

Maximizing UAV fog deployment efficiency for critical rescue operations: A multi-objective optimization approach

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.

Joint optimization of UAV position and user grouping for UAV‐assisted hybrid NOMA systems

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non‐orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non‐convexity, calling for an effective solution. To address this issue, a two‐stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory‐based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two‐stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.

Secrecy performance analysis on spatial modeling of wireless communications with unmanned aerial vehicle and ground devices

<span lang="EN-US">In this paper, the secrecy performance of the spatial modeling for ground devices with randomly placed eavesdroppers when an unmanned aerial vehicle (UAV) acted as two hops decode and forward (DF) was investigated. We characterize the secrecy outage probability (SOP) and intercept probability (IP) expressions. Our capacity performance analysis is based on the Rayleigh fading distributions. After analytical results by Monte Carlo simulation, and the Gauss-Chebyshev parameter was selected to yield a close approximation, the results demonstrate the SOP with the average signal-to-noise ratio (SNR) between UAV and ground users among the eavesdroppers and the IP relationship with the ability to intercept the information of the ground users successfully.</span>

Energy Minimization for UAV-Enabled Wireless Power Transfer and Relay Networks

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the quality of service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is non-convex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, simulation results confirm the superiority of our proposed algorithm.

Integration and differentiation: comparison of photography behaviors using unmanned aerial vehicle data in China and Europe

The continuous improvement in the design and manufacture of unmanned aerial vehicles (UAVs) has enabled them to assist humans in aerial tasks. At present, UAVs are not only used for environmental data collection, but also used by photographers to take photos. As the popularity of consumer UAVs rises, an increasing number of tourists are transitioning from the traditional perspectives of mobile phone and hand-held camera photography to the aerial perspective of UAV photography. Therefore this study examines UAV photos and related data uploaded by photographers within China and Europe to the UAV forum SkyPixel by using cloud-based data from Microsoft Azure vision, and sentiment, social network and spatial analyses. In addition, this paper also investigates the commonalities and differences between the behaviors and preferences of photographers who utilize UAV photography recreationally. The results indicate that the core content of UAV photography revolves around three major themes: natural landscapes, destination transportation facilities, and the daily behaviors of people. However, photographers in China mainly exhibit neutral emotions, while European photographers display mostly positive emotions. Hot spots are concentrated in mature tourist areas, especially top destinations near waterbodies. This study fosters a new understanding of the emerging group of UAV users.

Research on joint optimization of IRS-assisted UAV network

To address the problem of obstacles blocking wireless channels and increase the transmission rate of unmanned aerial vehicle (UAV) communication systems, this paper proposes a method for maximizing the system sum rate in downlink communication networks based on intelligent reflecting surface (IRS)-assisted UAV. First, we lead into non orthogonal multiple access (NOMA) technology. Second, the system sum rate is maximized by jointly optimizing the IRS phase shift, 3D UAV trajectory, user scheduling and power allocation. Since the coupling and non-convexity of optimization variables make this problem difficult to solve directly, the original nonconvex problem is converted into four subproblems through block coordinate descent (BCD). Then, the subproblems are solved alternately using methods, such as semi-definite relaxation technique (SDR), relaxation variable introduction, successive convex approximation (SCA), the Lagrange multiplier method and first-order Taylor expansion. The results show that the algorithm proposed in this paper substantially improves the system sum rate and has favorable convergence.

Sub-Connected Hybrid Precoding and Trajectory Optimization Using Deep Reinforcement Learning for Energy-Efficient Millimeter-Wave UAV Communications

In this letter, a sub-connected hybrid precoding system was designed to realize energy-efficient millimeter-Wave (mmWave) unmanned aerial vehicle (UAV) communications. Considering the limited capacity of the UAV battery, the system was jointly optimized with a UAV trajectory to increase the energy efficiency of the UAV under quality-of-service (QoS) and power budget constraints. The dynamic motion of the UAV changes the channel condition between the UAV and terrestrial users over time. Hence, a joint optimization problem was formulated as a non-convex and time-sequential domain, solved using deep reinforcement learning (DRL). The performances of the proposed scheme and a fully-connected hybrid precoding scheme were compared in terms of energy efficiency and show higher results.

IRS-Enabled Secure G2A Communications for UAV System With Aerial Eavesdropping

As one of the most promising new technologies in the upcoming 6G communication, intelligent reflecting surface (IRS) can significantly improve the reliability of wireless transmission and deal with the security threat in the presence of eavesdroppers by smartly reconfiguring the wireless propagation environment. However, existing research mainly focuses on terrestrial communication networks. In contrast, there is not much research on IRS-aided secure unmanned aerial vehicle (UAV) communication problems, especially for ground-to-air (G2A) communication scenarios with air eavesdroppers that will bring greater security threats. In this article, we consider an IRS-assisted G2A communication network with an altitude-dependent Rician fading channel, where the ground base station (BS) transmits legitimate information to the UAV user in the presence of air eavesdroppers. The transmission power, active and passive beamforming, and UAV 3-D trajectory are jointly optimized to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is used to solve it. Specifically, the overall optimization problem is divided into four subproblems, and nonconvex subproblems are solved by successive convex approximation and semidefinite relaxation algorithms. Simulation results show that the proposed alternating optimization algorithm has a good convergence performance and can increase the average secrecy rate by 28% compared with the scenario without IRS assistance.

Optimization of UAV 3D trajectory in a post-disaster area using dual energy-aware bandits

In a post-disaster area, Unmanned Aerial Vehicles (UAVs) are considered one of the most effective ways to provide emergency wireless communication services. In this letter, we investigate the UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV 3D flight trajectory to maximize the number of visited ground users during the flight round. Then, a dual-cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Finally, simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these constraints.

Investigation of a HAP-UAV Collaboration Scheme for Throughput Maximization via Joint User Association and 3D UAV Placement.

In this paper, a collaboration scheme between a high-altitude platform (HAP) and several unmanned aerial vehicles (UAVs) for wireless communication networks is investigated. The main objective of this study is to maximize the total downlink throughput of the ground users by optimizing the UAVs' three-dimensional (3D) placements and user associations. An optimization problem is formulated and a separate genetic-algorithm-based approach is proposed to solve the problem. The K-means algorithm is also utilized to find the initial UAV placement to reduce the convergence time of the proposed genetic-algorithm-based allocation. The performance of the proposed algorithm is analyzed in terms of convergence time, complexity, and fairness. Finally, the simulation results show that the proposed HAP-UAV integrated network achieves a higher total throughput through joint user association and UAV placement schemes compared to a scheme with a single HAP serving all users.

Priority-Aware Resource Scheduling for UAV-Mounted Mobile Edge Computing Networks

In this paper, we investigate the joint impact of task priority and mobile computing service on the mobile edge computing (MEC) networks, in which one unmanned aerial vehicle (UAV) provides mobile computing service to help compute the tasks from users in multiple hotspots where the task priority is time-varying. For such a system, we firstly measure the system performance by the computing utility of multiples users, where the effect of a wide-range task priority is incorporated. We then analyze the impact of the network wireless bandwidth and UAV computational capability on the system performance, from which we optimize the system through UAV hotspot selection and user task offloading. To solve the optimization problem, we further employ deep Q-learning algorithm to learn an effective solution by continuous interaction between the UAV agent and system environment. Simulations are finally conducted to verify the superiority of the proposed studies in this paper.

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.

On performance of RIS-aided ground-to-air and air-to-ground communications in multi-user NOMA systems

This paper examines the performance of a wireless system where two reconfigurable intelligent surfaces (RISs) and an unmanned aerial vehicle (UAV) are combined to aid multiple ground users adopting nonorthogonal multiple access (NOMA) scheme. Specifically, the UAV acts as an aerial relay station, the first and the second RISs are respectively used to aid ground-to-air (G2A) and air-to-ground (A2G) communications. Besides the reflected link from the RISs, the direct link between a ground base station (S) and the UAV, the UAV and ground users are exploited to increase the received power at UAV and ground users. We successfully derive the theoretical formulas of outage probability (OP) and ergodic capacity (EC) of RIS-aided G2A/A2G-NOMA system over Nakagami-m fading channel. The derived formulas are validated by Monte-Carlo simulations, and the impacts of system parameters on the OP and EC are fully evaluated to achieve useful insights into the system behaviors. Numerical results clarify that by using two RISs where every RIS is equipped with 100 reflecting elements (REs), the OP of RIS-aided G2A/A2G-NOMA system is greatly lower than the OP of the G2A/A2G-NOMA system without RISs, in which there are only S-UAV and UAV-ground users direct links. In the case of EC, the benefit of using RISs focuses on a low transmit power regime. It is because the EC of the RIS-aided G2A/A2G-NOMA system is only higher than the EC of the system without RISs in the low transmit power region. For the high transmit power regions, the ECs with and without RISs are similar.

Coverage Analysis for Cellular-Connected Random 3D Mobile UAVs With Directional Antennas

This letter proposes an analytical framework to evaluate the coverage performance of a cellular-connected unmanned aerial vehicle (UAV) network in which UAV user equipments (UAV-UEs) are equipped with directional antennas and move according to a three-dimensional (3D) mobility model. The ground base stations (GBSs) equipped with practical down-tilted antennas are distributed according to a Poisson point process (PPP). With tools from stochastic geometry, we derive the handover probability and coverage probability of a random UAV-UE under the strongest average received signal strength (RSS) association strategy. The proposed analytical framework allows to investigate the effect of UAV-UE antenna beamwidth, mobility speed, cell association, and vertical motions on both the handover probability and coverage probability. We conclude that the optimal UAV-UE antenna beamwidth decreases with the GBS density, and the omnidirectional antenna model is preferred in the sparse network scenario. What's more, the superiority of the strongest average RSS association over the nearest association diminishes with the increment of GBS density.

Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Unmanned aerial vehicles (UAVs) play an important role in the future of 5G and 6G communication networks. UAV-assisted communication offers the benefits of improved network capacity and coverage. A typical communication setup is for UAVs to connect users to the core network via a backhaul channel. Some of the challenges in such a setup include user-UAV association and management of the backhaul channel. These two challenges are greatly impacted by the positioning of the UAVs in the network. In this article, we address these challenges by considering a joint UAV placement and user association problem under data rate, signal to interference and noise ratio, and bandwidth constraints. To overcome this problem, a hybrid PSO-K-means clustering algorithm is used in two stages. In the first stage, we use a K-means algorithm to cluster users and determine their horizontal locations. In the second stage, we use particle swarm optimization (PSO) to find the efficient 3D position of UAVs to maximize various network designs, namely, the network-centric approach and the user-centric approach. The performance of the proposed solution is verified using simulation results.

UAV Trajectory Optimization in a Post-Disaster Area Using Dual Energy-Aware Bandits.

Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV's flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints.