Multi‐Objective Resource Optimization in UAV‐Enabled Heterogeneous Cellular Networks Using Serverless Federated Learning and Power‐Domain NOMA

Terahertz (THz) communication networks, combined with Intelligent Reflecting Surfaces (IRS), hold significant potential for advancing the capabilities of Sixth-Generation (6G) wireless systems. The integration of these technologies offers opportunities for ultra-fast data transmission and substantial improvements in network capacity, meeting the growing demand for higher bandwidth and faster connectivity. This study investigates the use of flying IRS-assisted Unmanned Aerial Vehicles (UAVs) within a THz communication framework, aiming to overcome challenges in environments with complex signal propagation, such as urban settings. To facilitate this integration, we introduce the Fly-IRS algorithm, designed to optimize key aspects of network performance, including user device grouping, IRS phase shifts, and UAV positioning. These factors are critical for maximizing data transmission rates, boosting network capacity, and minimizing service disruptions that affect user experience. Our approach leverages a combination of Multi-Agent Deep Reinforcement Learning (MADRL) and Particle Swarm Optimization (PSO) to effectively handle user grouping, IRS phase shift optimization, and UAV trajectory planning. By merging the strengths of both techniques, the Fly-IRS algorithm enhances system adaptability and efficiency. Simulation results demonstrate significant improvements in data rates, with the Fly-IRS algorithm delivering up to 10% better performance compared to the MADRL method alone. These findings highlight the transformative potential of integrating THz communication networks with IRS and UAV technologies, emphasizing their ability to shape future wireless communication systems. This innovation could lead to more reliable, high-capacity networks, capable of addressing the advanced requirements of 6G, offering improved service quality even in difficult environments.

The use of unmanned aerial vehicles (UAVs) for collecting data from sensors has garnered considerable attention in both academic and industrial fields in recent years. To assess the freshness of the information gathered, the age of information (AoI) metric has emerged as an important tool, particularly in time-sensitive applications such as traffic management or monitoring critical issues like fire hazards. This study explores a scenario where a UAV, supported by multiple intelligent reflecting surfaces (IRSs), periodically gathers data from distributed internet of things devices (IoTDs). To minimize the average AoI across all devices, a comprehensive optimization approach is employed. Key elements such as the UAV’s trajectory, device transmission scheduling, timeslot duration, IRS selection factor, and the phase shift matrix of the selected IRS are jointly optimized. However, the problem’s complexity arises from its mixed-integer nature—IRS selection and device scheduling coefficients require integer values—and the strong interdependence of channel conditions with the UAV’s flight path and IRS phase shifts. These challenges make the problem difficult to solve. To address this, the primary optimization problem is framed as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based approach is then adopted to determine the optimal UAV trajectory, device scheduling, and timeslot duration. Additionally, a location-based method is employed for selecting the suitable IRS, while a closed-form solution is used to design the phase shift matrix of the selected IRS. Results evidence improvements in successful transmission and fairness selection of devices when using multiple IRSs and variable timeslots.

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

Wireless communication environment becomes more and more complex especially when facing vicious eavesdroppers and external jammers. Intelligent reflecting surface (IRS) can provide secure or anti-jamming communication approaches via passively reflecting with low power consumption and high cost efficiency. Unmanned aerial vehicle (UAV) also can be used to cooperatively transmit useful signals of legitimate users with vicious attackers. Carrying IRS on UAV can add new cooperative strategies, such as trajectory or height of UAV, numbers of reflecting elements or phase shift of IRS, to improve secure and anti-jamming performance of legitimate users. Two cooperative transmission schemes assisted with UAV and IRS are proposed to confront passive eavesdroppers or smart jammers in this paper. The first kind of cooperation is to select UAV relay or IRS reflecting strategy to improve energy efficiency of system with passive eavesdroppers. The second kind of cooperation is to jointly optimize phase shifts of IRS and trajectory of UAV to enhance transmission capacity with smart jammers. Simulation results show that, with the help of the cooperation of UAV and IRS, secure and reliable communications can be realized and system performance can be improved compared with no cooperation scheme or only one kind of cooperation with UAV or IRS scheme.

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay; 2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability; 3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system.

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS–UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.

This study aims to consider the improvement of technical inspection methods in the context of military conflict. Taking into account the factor of human safety in the survey process, the search for optimal methods and technologies for data collection and analysis is carried out, in particular, the use of modern technologies such as unmanned aerial vehicles (UAVs) and geographic information systems (GIS) is considered. The use of drones allows for quick and efficient surveys of hard-to-reach areas and the collection of high-quality damage data. The integration of UAVs with GIS provides a systematic view of large-scale destruction problems and helps to make informed decisions on how to address the consequences of armed aggression. Despite the advantages of using these technologies, their limitations should be taken into account. The combination of different survey methods allows to obtain the most complete and accurate information about the condition of buildings and structures, which is key in making decisions on the restoration and reconstruction of the affected areas. The study examined the practical use of these technologies within the framework of the Rebuild ua project, which digitized the main destructions in Chernihiv, Sumy, and Kyiv regions. The use of unmanned aerial vehicles in combination with geographic information technologies allows to systematize, analyze, obtain visual information and images with geometric data, which simplifies the process of damage assessment and decision-making during the survey procedure. GIS technologies are also capable of analyzing geographic data, visualizing damage on a map, locating damaged objects and assessing their damage. The integration of GIS technologies and unmanned aerial vehicles with other systems makes it possible to simplify decision-making on the priorities of liquidation and recovery.

In this paper, a novel three dimensional (3D) positioning framework of fluid antenna system (FAS)-enabled unmanned aerial vehicles (UAVs) is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-enabled passive UAVs cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs via the reflection from the target UAV. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the base station (BS), which calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate this problem as an optimization problem whose goal is to minimize the target UAV positioning error via optimizing the trajectories of all controlled UAVs and antenna port selection of passive UAVs. To address this problem, an attention-based recurrent multi-agent reinforcement learning (AR-MARL) scheme is proposed, which enables each controlled UAV to use the local Q function to determine its trajectory and antenna port while optimizing the target UAV positioning performance without knowing the trajectories and antenna port selections of other controlled UAVs. Different from current MARL methods that use feedforward neural networks to approximate Q functions, the proposed method uses a recurrent neural network (RNN) that incorporates historical state-action pairs of each controlled UAV, and an attention mechanism to analyze the importance of these historical state-action pairs, thus improving the global Q function approximation accuracy and the target UAV positioning accuracy. Simulation results show that the proposed scheme can reduce the average positioning error by up to 17.5% and 58.5% compared to the value decomposition based-MARL scheme with FAS and the proposed AR-MARL method without FAS.

In this paper, we investigate the reliability of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications in the case of limited UAV energy. Under constraints of the UAV energy and the channel decoding error rate, we formulate a reliability maximization problem by jointly optimizing the IRS’s scheduling, the UAV’s trajectory, the IRS’s phase shift, and the UAV’s transmit power. Since the partial constraints of the problem are strictly nonconvex and its variables are coupling, the problem is difficult to convert to a nonconvex problem. Therefore, we propose a chaotic adaptation hybrid whale optimization algorithm (CAHWOA) to solve the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the binary optimization algorithm (BWOA). Simulation results demonstrate that the joint optimization of IRS and UAV can improve the system communication reliability by almost 32% compared with the two baseline schemes. CAHWOA can improve the convergence rate by nearly 20% and enhance the optimization-seeking accuracy by about 0.04 compared with the three baseline algorithms.

This paper describes an accurate attitude determination technique using multiple global navigation satellite system (GNSS) receivers and antennas for small unmanned aerial vehicles (UAVs). A laser survey using a small UAV needs highly accurate position and attitude information for reconstructing 3D models. The proposed technique uses the multiple GNSS antenna's positions computed by the GNSS carrier phase measurements to estimate the UAV's position and attitude. To solve the GNSS carrier phase ambiguity, the reliable ambiguity search is performed with a couple of constraints for the base line vector. The performance of the proposed technique is evaluated in the static test. From the test, the proposed technique could estimate UAV's attitude in 0.1 degree accuracy. Furthermore, the accurate UAV's position could be estimated in almost 100 % availability.

Localization for the indoor aerial robot remains a challenging issue because global positioning system (GPS) signals often cannot reach several buildings. In previous studies, navigation of mobile robots without the GPS required the registration of building maps beforehand. This paper proposes a novel framework for addressing indoor positioning for unmanned aerial vehicles (UAV) in unknown environments using a camera. First, the UAV attitude is estimated to determine whether the robot is moving forward. Then, the camera position is estimated based on optical flow and the Kalman filter. Semantic segmentation using deep learning is carried out to get the position of the wall in front of the robot. The UAV distance is measured using the comparison of the image size ratio based on the corresponding feature points between the current and the reference of the wall images. The UAV is equipped with ultrasonic sensors to measure the distance of the UAV from the surrounded wall. The ground station receives information from the UAV to show the obstacles around the UAV and its current location. The algorithm is verified by capture the images with distance information and compared with the current image and UAV position. The experimental results show that the proposed method achieves an accuracy of 91.7% and a computation time of 8 frames per second (fps).

: With the recent draw down of the US military after the end of the Cold War, the US Department of Defense (DoD) is placing considerable emphasis on employing a lighter, leaner, and more lethal military force to accomplish the strategic objectives mandated by political leadership. As a force enabler for military forces, Unmanned Aerial Vehicles (UAVs) recently demonstrated their potential during NATO military operations in Kosovo. Their activities were limited to merely gathering enemy intelligence. As a result of advancements in UAVs capabilities, UAVs expanded their operations by directly assisting in combat missions. The resulting logical question that evolved out of the Kosovo operations is, can UAVs be more actively integrated into other military functions to enhance mission accomplishment? More specifically, can UAVs be incorporated into the function of gaining and maintaining control of the air for US forces? To completely answer this question, this research paper will first analyze the current background of UAVs as seen in recent military operations. Secondly, the function of counterair will be examined to identify the deficiencies US forces have in obtaining control of the air. Third, the present and future capabilities that UAVs can bring to the fight will be identified. Fourth, a detailed examination of which UAVs payloads can be incorporated into the function of counterair will be accomplished. Fifth, this paper will show that UAVs can be effectively integrated to enhance US military weapons systems accomplishment of the function of counterair. Finally, this author will briefly discuss one possible plan to integrate UAVs in the function of counterair to overcome the noted deficiencies.

Integration of Cognitive Radio Technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges

With technological development of multi sensors, UAV (unmanned aerial vehicle) can identify and locate key targets in essential monitoring areas or geological disaster-prone areas by taking video sequence images, and precise positioning of the video sequence images is constantly a matter of great concern. In recent years, precise positioning of aerial images has been widely studied. But it is still a challenge to simultaneously realize precise, robust and dynamic positioning of UAV’s patrolling video sequence images in real time. In order to solve this problem, a visual positioning model for patrolling video sequence images based on DOM rectification is proposed, including a robust block-matching algorithm and a precise polynomial-rectifying algorithm. First, the robust block-matching algorithm is used to obtain the best matching area for UAV’s video sequence image on DOM (Digital Orthophoto Map), a pre-acquired digital orthophoto map covering the whole UAV’s patrolling region. Second, the precise polynomial-rectifying algorithm is used to calculate accurate rectification parameters of mapping UAV’s video sequence image to the best matching area obtained above, and then real time positioning of UAV’s patrolling video sequence images can be realized. Finally, the above two algorithms are analyzed and verified by three practical experiments, and results indicate that even if spatial resolution, surface specific features, illumination condition and topographic relief are significantly different between DOM and UAV’s patrolling video sequence images, proposed algorithms can still steadily realize positioning of each UAV’s patrolling video sequence image with about 2.5 m level accuracy in 1 s. To some extent, this study has improved precise positioning effects of UAV’s patrolling video sequence images in real time, and the proposed mathematical model can be directly incorporated into UAV’s patrolling system without any hardware overhead.