CoMP in the Sky: UAV Placement and Movement Optimization for Multi-User Communications

Due to the characteristics of high maneuverability, low cost, and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) have attracted more and more attention in the field of communications recently. Using UAVs as a mobile aerial base station (BS), commonly referred to as UAV-aided communication, can provide a quick and flexible solution for sudden communication needs and coverage holes. To maximize the coverage effect, effective three-dimensional (3-D) deployment is crucial. As a result of the complexity of air to ground channels and interference between UAV s and BSs, 3- D placement of UAVs is currently one of the main challenges for UAV-aided communications. In this paper, Taguchi's method is used for 3-D placement optimization of multiple UAV s, aiming to make up for the coverage holes and assist the ground cellular network to cover the area as seamlessly as possible. Simulation results indicate that Taguchi's method can effectively improve the coverage ratio of the area by optimizing the 3-D placement of UAV s, and the optimization effect is better than simulated annealing alagrithm.

Unmanned aerial vehicle (UAV) communication is one of the enabling technology in the 5G/B5G era, which has received extensive research attention recently. In this paper, we consider a UAV employing amplify-and-forward protocol to facilitate the secure transmissions for a legitimate user pair, where the direct link is not available. In order to protect the legitimate transmissions and prevent the eavesdropper from intercepting the source message, the legitimate receiver launches a jamming signal to confuse the eavesdropper. In this regard, the optimal jamming power and UAV placement are jointly investigated towards the highest secrecy rate. For this problem, we first propose an efficient power control scheme based on bi-section search with fixed UAV placement. Then, a deep neural network (DNN) is constructed and trained based on the data from exhaustive search in order to determine the best UAV placement. Through numerical simulations, we verify that the trained DNN effectively approximates the optimal UAV placement, also demonstrated is the evident superiority of our proposed scheme as compared to the baselines in terms of security performance.

A novel framework for dynamic 3-D deployment of unmanned aerial vehicle (UAV) in the aerial base station system (ABSS) that based on the machine learning algorithms is proposed. In the framework, the UAV is deployed as an aerial base station to serve a group of ground users and is placed based on the prediction of the users' mobility. The joint problem of prediction of users' track and 3-D deployment of the UAV is formulated for maximizing the sum transmit rate. A two-step approach is proposed for predicting the movement of users and for determining the dynamic 3-D placement of the UAV. Firstly, an echo state network (ESN) based prediction algorithm is utilized for predicting the future positions of users based on the real-world datasets collected from Twitter. Secondly, an iterative K-Means based algorithm is proposed for obtaining the optimal placement of UAV at each time slot based on the output of ESN model. Numerical results are illustrated for showing the superiority of the proposed algorithm over the prevalent algorithm on prediction tasks. The accuracy and efficiency of the proposed framework are also investigated. Additionally, compared with static placement of the UAV, the advantage of dynamic 3-D deployment is demonstrated.

Due to their portability and ability to be deployed at low altitudes, unmanned aerial vehicles (UAVs) are a viable option for usage as mobile base stations in the air. The channels between UAVs and ground users pose a threat of substantial co-channel interference in the network with the deployment of UAVs due to line-of-sight links. This research develops coordinated multi-point (CoMP) technique based on joint transmission approach in a UAV-enabled cellular system to handle this problem, coupled with the proper positioning of UAVs. Although CoMP has the potential to improve network performance, its processing time and overhead make it challenging to adopt across the network. Therefore, a user-centric clustering strategy is used to randomly disperse the network’s nodes into overlapping groups. By optimizing CoMP clusters, beamforming vectors, and UAV flight paths, we aim to keep communication energy usage to a minimum. The optimization problem is formulated, considering the limitations imposed by the UAVs’ mobility and maximum transmit power. An effective method is proposed to deal with the nonconvexity of the formulated problem. First, a circular filling strategy is used to determine the flight trajectories of the UAVs. Then, we reformulate the non-convex constrained problem into an equivalent convex optimization problem and optimize the CoMP clusters and the beamforming vectors jointly. The effectiveness of the proposed solution has been numerically validated.

We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network consisting of multiple cooperative UAVs, whose operations are coordinated by a central ground controller using wireless fronthaul links, to serve multiple ground user equipments (UEs). A problem of jointly designing UAVs’ positions, transmit beamforming, as well as UAV-UE association is formulated in the form of mixed integer nonlinear programming (MINLP) to maximize the sum UEs’ achievable rate subject to limited fronthaul capacity constraints. Solving the considered problem is hard owing to its non-convexity and the unavailability of channel state information (CSI) due to the movement of UAVs. To tackle these effects, we propose a novel algorithm comprising of two distinguishing features: (i) exploiting a deep Q-learning approach to tackle the issue of CSI unavailability for determining UAVs’ positions, (ii) developing a difference of convex algorithm (DCA) to efficiently solve for the UAV’s transmit beamforming and UAV-UE association. The proposed algorithm recursively solves the problem of interest until convergence, where each recursion executes two steps. In the first step, the deep Q-learning (DQL) algorithm allows UAVs to learn the overall network state and account for the joint movement of all UAVs to adapt their locations. In the second step, given the determined UAVs’ positions from the DQL algorithm, the DCA iteratively solves a convex approximate subproblem of the original non-convex MINLP problem with the updated parameters, where the problem’s variables are transmit beamforming and UAV-UE association. Numerical results show that our design outperforms the existing algorithms in terms of algorithmic convergence and network performance with a gain of up to 70%.

Unmanned aerial vehicle (UAV) communication is regarded as a promising technology for lightweight Internet of Things (IoT) communications in narrowband-IoT (NB-IoT) systems deployed in rugged terrain. In such UAV-assisted NB-IoT systems, the optimal UAV placement and resource allocation play a critical role. Consequently, the joint optimization of the UAV placement and resource allocation is considered in this study to improve the system capacity. Because the considered optimization problem is an NP-hard problem and owing to its non-convex property, it is difficult to optimize both the UAV placement and resource allocation simultaneously. Therefore, a competitive clustering algorithm has been developed by exchanging strategies between the UAV and the adjacent IoT devices to optimize the UAV placement. With multiple iterations, the UAV and the IoT devices within the coverage area of the UAV, converge their clustering strategies, which are suboptimal, to satisfy both sides. The bordering IoT devices of the adjacent clusters are then migrated heuristically toward each other to obtain the optimal system capacity maximization. Finally, the transmission throughput is optimized using the Nash equilibrium. The simulation results demonstrate that the algorithms proposed in this study exhibit rapid convergence, within 10 iterations, even in a large environment. The performance evaluation demonstrates that the proposed scheme improves the system capacity of the existing schemes by approximately 28%.

In recent years, as the cost of Unmanned Aerial Vehicle (UAV) technology has steadily decreased and advancements in data processing speed and image recognition have accelerated, UAVs have become increasingly favored in civil engineering surveying and mapping. The purpose of this paper is to study the development of UAV technology in the field of civil engineering surveying and mapping and its current application methods. By analysing the above contents, the problems still existing in the current practical use of UAV technology are studied. Through a review of relevant literature, newly developed technologies will be integrated with current UAV capabilities to propose recommendations for future improvements. This article employs literature analysis and case study methodologies to investigate the utilization of UAV technology. It delineates the current state of development and the challenges that UAV technology faces in the surveying field by examining extant academic research materials and legal regulations. It also provides a concise overview of the distinctive benefits of UAV technology. Simultaneously, the deficiencies of UAV technology in field environments are investigated, particularly focusing on data collection and accuracy in engineering surveying projects. UAV technology has obvious advantages compared with traditional methods in the field of civil engineering surveying and mapping. It can complete surveying and mapping tasks efficiently and quickly in many application scenarios. The high flexibility of UAVs and the high-precision scanning of their sensors are enough to meet the needs of current engineering surveying and mapping. The wide application of UAV technology still faces problems such as imperfect laws and regulations, insufficient surveying and mapping scope, and large surveying and mapping data. Addressing these issues requires the development of new laws and the creation of algorithms to enhance data processing capabilities.

In the last few years, a considerable amount of research effort has been put into creating networks of Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) and Internet of Drones (IoDs) are the result of such efforts. FANETs assume that the member UAV nodes belong to different users. However, in UAV networks that would find their applications in areas such as agriculture, environment monitoring, crowd surveillance and natural disaster situations, all UAVs would belong to the same organization, creating a private UAV network. The number of UAVs used in private UAV networks is relatively small compared to that of FANETs due to the cost. If the packet routing approaches designed for FANETs are used in such networks, the quality of video transmitted through the network will suffer. This is because, while there can be many neighbors for each UAV in a FANET, the movements of UAVs in a private UAV network may cause some UAVs not to have any neighbors. To overcome this problem, a UAV in such a network needs to be controlled differently and an appropriate method of selecting the next hop node is required. The new approach presented in this paper uses mathematical analysis to find the optimum next hop node in a private UAV network by reducing a global optimization problem to a local optimization problem at each UAV node along the path from the source to the destination. Once the best next hop node is found, the UAVs can be controlled accordingly.

Unmanned aerial vehicles (UAVs)-based projects in the smart city paradigm are no more a slim chance. Crisis reaction and coordination, open or private infrastructure control, coordination or support to astute transportation systems are a portion of spaces in which UAV may end up key in the city of things. UAVs have pulled in a critical consideration in an assortment of fields, particularly in intelligent transportation systems. UAVs speak of a vital help to the current advancements to control street traffic and screen occurrences. The chapter investigates the integration of UAVs and future communication networks, such as 5G as part of the existing internet of things and cloud platforms. It aims at proposing an optimized UAV placement that assures the aforementioned characteristics. A framework is proposed for optimizing three dimensional placements and the mobility of UAVs. UAVs have pulled in a critical consideration in an assortment of fields, particularly in intelligent transportation systems.

In the zenith-horizon placement for achieving minimum geometric dilution of precision (GDOP), one access node or sensor is positioned along the z-axis, while the remaining nodes are placed symmetrically on a three-dimensional (3D) cone. This configuration yields the minimum GDOP at the cone’s tip, which we term the designated min-GDOP point. However, in practical localization applications, the unknown node is not necessarily located at this designated min-GDOP point; instead, it may be situated anywhere within an area. As a result, evaluating localization accuracy across the entire area, rather than at a single point, is more relevant. Averaged horizontal dilution of precision (HDOP) and GDOP across the region provide more meaningful metrics for system-wide performance than values computed only at a specific location. Although many recent positioning applications leverage multiple unmanned aerial vehicles (UAVs), many established fixed sensor deployments predate the widespread adoption of UAVs. This paper proposes a novel approach with a single UAV working in conjunction with existing fixed access nodes for positioning. This approach offers improved adaptability for fixed infrastructure while circumventing the expense of establishing entirely new UAV systems, thus providing a valuable compromise. We investigate the criteria of average HDOP and GDOP over the given area. The objective is to determine optimal UAV positions along the flight path that minimize the average HDOP and/or GDOP across the area. Due to the analytical complexity, we employ numerical methods. Our simulation results demonstrate that minimizing average HDOP and GDOP often requires different UAV positions, depending on the number of access nodes and the size of the area. Consequently, achieving simultaneous minimization of both metrics with a single UAV trajectory is generally infeasible. When minimizing the average HDOP with a small number of access nodes, aligning the UAV’s XY-plane angle with those of the stationary nodes, offset by , proves advantageous. This angular alignment becomes less critical as the number of access nodes increases. For scenarios where both HDOP and GDOP are important, UAV placement can be optimized by selecting appropriate trade-offs. Additionally, we quantify how increasing the number of access nodes improves the average HDOP and GDOP over the specified area.

Unmanned aerial vehicle (UAV) can be combined with virtual reality (VR) to collect the rapidly changing physical environment by leveraging its high flexibility, so as to provide an immersive experience for metaverse users. In this letter, we study the UAV placement problem in UAV-assisted VR systems where UAVs are deployed to capture images of ground targets and then transmit them to ground users for virtual environment reconstruction. The goal is to minimize the transmission delay while satisfying the image resolution requirement. Firstly, the UAV placement problem is formulated into a non-convex optimization problem. Then, an efficient algorithm is proposed to obtain a high-quality solution by adopting the block coordinate descent and successive convex approximation techniques with low complexity. Finally, the numerical results are provided and show that considerable benefits can be obtained with a suboptimal UAV placement. Specifically, the proposed scheme can reduce the transmission delay by over 15 ms compared to the conventional scheme when the received signal-to-noise ratio at the reference distance is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{6}$ </tex-math></inline-formula> and the resolution requirement is 0.5.

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes.

Analytical expressions of control laws for a linearized model of short-period longitudinal motion of a third-order unmanned aerial vehicle are obtained. The synthesis is based on the original decomposition of the control object and the method of modal state-based control of a dynamic system, previously developed on its basis. The numerical modeling results of the aircraft short-period longitudinal motion control by using analytically synthesized control laws, which are graphs of transient processes and control actions, are presented. Keywords decomposition, modal synthesis, longitudinal short-period motion of an aircraft, poles of a dynamic system

In this paper, we consider the unmanned aerial vehicles (UAVs) as mobile sensors with flying ability to provide a timely and accurate vision over a large supervised area. The interconnection of these UAVs, in order to route the collected data to the control station on the ground, constitutes the flying ad hoc network (FANET). However, the UAVs features impose challenging issues to data collection and routing. Thus, the communications are prone to high-frequency link disconnections caused by the high mobility of UAVs. In this paper, we propose a reliable architecture to collect and route data to a ground station (GS). The proposed simple area division cluster-based algorithm (SAD-CA) provides a stable network architecture through the clustering technique in order to deal with the high mobility of the UAVs while preserving their energy consumption. The performance evaluation of the proposed algorithm, carried out using the network simulator 3 (NS3), has shown convincing results, in terms of energy consumption and network lifetime, outperforming those exhibited by the referred protocol.