Terahertz Integrated Sensing and Communication-Empowered UAVS in 6G: A Transceiver Design Perspective

Unmanned Aerial Vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV relay network where the UAV is employed as an amplify-and-forward relay to provide communication service for a group of buoy sensors far off the coast in a three-dimensional (3D) coordinate system. Once a high-speed relay communication link is established, stable transmission is critical. We jointly optimize buoy sensors communication scheduling, UAV trajectory and the system power to minimize the maximum average outage probability among all buoy sensors. The formulated problem is a mixed integer non-convex optimization problem that is difficult to solve in general. We decouple the problem into three sub-problems, which can be iteratively solved by utilizing the inexact block coordinate descent and successive convex optimization techniques. Simulation results show that the outage probability of the proposed algorithm is significantly lower than that of the circle trajectory for the UAV relay.

Terahertz (THz) communications are promising to enable backhaul data transmission in cellular connected unmanned aerial vehicle (UAV) networks by providing ultra-high transmission data rate. In such networks, connected autonomous UAVs (CA-UAV) have the potential to build air-ground networks and achieve seamless wide-area coverage with little or no human assistance. With the usage of high frequency (i.e., THz), radio/radar sensing function is expected to be achieved in wireless networks, which can be used to track motion control of UAV for beam tracking in THz communications. However, it is extremely difficult to jointly design sensing, communication, and motion control since they have been developing in relatively parallel with limited intersections. In this paper, we propose a new joint scheduling method of sensing, communication, and control for beam alignment in THz communications enabled CA-UAV networks to support data transmission of the backhaul from UAV to the ground base station (BS). In the proposed method, we provide a new definition from motion control perspective, i.e., state-to-noise-ratio, which links the relationship among sensing, motion control of CA-UAV, and data rate determined by beam alignment in THz communications. Then, a closed-form expression is obtained for data rate triggered sensing-control pattern activation design, where both data rate requirement in THz communications and motion control performance of UAV are guaranteed. Simulation results show remarkable performance of the proposed method.

Terahertz (THz) communications have the potential to provide ultra-high transmission data rate for applications with high rate requirements, e.g., virtual reality, augmented reality, and autonomous vehicles. However, it is very easy to be blocked and misaligned for THz transmissions due to its ultra-high frequency and narrow beamwidth. To deal with the blocked issue, we adopt reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicles (UAV) to achieve seamless wide-area coverage with little or no human assistance. Furthermore, to deal with the misaligned issue in mobile RIS-assisted UAV scenarios, we propose a novel joint motion control and communication scheduling method in the aforementioned scenario, which can maximize the sum rate of all users while guaranteeing data rate requirement for each user and maintaining good motion control performance for UAV networks. Specifically, we first obtain a closed-form expression for data rate triggered motion control design, where both data rate requirement in THz communications and motion control performance of UA V are guaranteed. Then, the sum rate is maximized for all users based on the motion control performance. Simulation results show remarkable performance of the proposed method.

Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering the limited energy budget of UAVs and the existence of multiple jammers, we introduce a simultaneous wireless information and power transfer (SWIPT) technology and study the problems of joint-trajectory planning, time, and power allocation to increase communication performance. Specifically, the network includes multiple UAVs, source nodes (SNs), destination nodes (DNs), and jammers. We assume that the UAVs need to communicate with DNs, the SNs use the SWIPT technology to transmit wireless energy and information to UAVs, and the jammers can interfere with the channel from UAVs to DNs. In this network, our target was to maximize the throughput of DNs by optimizing the UAV’s trajectory, time, and power allocation under the constraints of jammers and the actual motion of UAVs (including UAV energy budget, maximum speed, and anti-collision constraints). Since the formulated problem was non-convex and difficult to solve directly, we first decomposed the original problem into three subproblems. We then solved the subproblems by applying a successive convex optimization technology and a slack variables method. Finally, an efficient joint optimization algorithm was proposed to obtain a sub-optimal solution by using a block coordinate descent method. Simulation results indicated that the proposed algorithm has better performance than the four baseline schemes.

Aim: The rapid growth in the number of ground users over recent years has introduced the issues for a base station of providing more reliable connectivity and guaranteeing the reasonable quality of service (QoS). Thanks to the unique features of unmanned aerial vehicles (UAVs), such as flexibility in deployment, large coverage range and lower cost, UAVs can help the base station to provide wireless connectivity to the ground users, e.g., in rural and remote areas. As the energy limitation is the main concern for UAVs, the motivation is to provide uninterrupted connection to ground users in the next generation wireless networks using solar powered UAV-assisted air networks. Methods: The research uses global horizontal irradiance (GHI) data from the National Renewable Energy Laboratory, small cell power ratings for communication, and UAV parameters. In addition, the TensorFlow library and Python programming language were also used to develop machine learning models and simulate the UAV flying time. Results: In this paper, we develop a novel resource management system for UAVs, which consists of an energy harvesting deep learning model to predict the future power harvested from the solar panel and a consumption model which determines user arrival rate. With energy consumption and harvesting predictions, the resource management system adaptively switches the power consumed by a UAV for communication. In addition, based on the future energy availability and user's arrival rate, the resource management system communicates with other UAVs and enables energy coordinating scheduling among multiple UAVs to support user communications. The experiment results demonstrate that by using adaptive energy scheduling among UAVs, the flying time of the UAVs is improved by 40% during nighttime and by 37% when performing energy coordination among multiple UAVs. Conclusion: In this work, the UAV based communications have been researched. To understand more about UAVs and air segments, some literature review has been done based on previous works. Finally, alteration of the transmission power using several methodologies has been accomplished to increase the flying time of the UAV.

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV-enabled wireless network where the UAV is employed as an aerial mobile base station (BS) to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users by jointly optimizing the multiuser communication scheduling and UAV trajectory over a finite horizon. The formulated problem is shown to be a mixed integer non-convex optimization problem that is difficult to solve in general. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques, which is guaranteed to converge to at least a locally optimal solution. To achieve fast convergence and stable throughput, we further propose a low-complexity initialization scheme for the UAV trajectory design based on the simple circular trajectory. Extensive simulation results are provided which show significant throughput gains of the proposed design as compared to other benchmark schemes.

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations (BSs) are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAVs' trajectory and power control. The formulated problem is a mixed integer non-convex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the non-convex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge to at least a locally optimal solution. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.

Integrating wireless-powered Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) leverages computation offloading services for mobile devices, significantly enhancing the mobility and control of MEC networks. However, current research has not focused on customizing system designs for Terahertz (THz) communication networks. When dealing with THz communication, one must account for blockage vulnerability due to severe THz wave propagation attenuation and insufficient diffraction. The Intelligent Reflecting Surface (IRS) can effectively address these limitations in the model, enhancing spectrum efficiency and coverage capabilities while reducing blockage vulnerability in THz networks. In this paper, we introduce an upgraded MEC system that integrates IRS and UAVs into THz communication networks, focusing on a binary offloading policy for studying the computation offloading problem. Our primary objective is to optimize the energy consumption of both UAVs and User Electronic Devices, alongside refining the phase shift of the IRS reflector. The problem is a Mixed Integer Non-Linear Programming problem known as NP-hard. To tackle this challenge, we propose a two-stage deep learning-based optimization framework named Iterative Order-Preserving Policy Optimization (IOPO). Unlike exhaustive search methods, IOPO continually updates offloading decisions through an order-preserving quantization method, thereby accelerating convergence and reducing computational complexity, especially when handling complex problems with extensive solution spaces. The numerical results demonstrate that the proposed algorithm significantly improves energy efficiency and achieves near-optimal performance compared to benchmark methods.

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.

An unmanned aerial system (UAS), consisting of unmanned aerial vehicles (UAVs) with wireless transceivers, has been considered as an indispensable complement to conventional terrestrial communication infrastructure that cannot fully meet mobile users' demand on ubiquitous connectivity, particularly in some practical areas, such as complex terrains, disaster areas, and temporary traffic hotspots. Thanks to UAVs' high maneuverability and flexible deployment, a UAS can be deployed in these areas to offer ubiquitous connectivity in a timely and cost-effective way. However, due to the limited onboard energy storage capacity, UAVs have to frequently land for energy replenishment, which inevitably affects the provisioning of wireless connectivity services. To prolong UAVs' hovering time, adopting solar energy to power them is an alternative way. In this paper, we explore the joint control of routing, data rate, and transmission power to minimize the energy consumption rate of relaying data in solar-powered UASs. The energy consumption rate minimization problem is formulated as a nonlinear programming (NLP), which is generally NP-hard. To efficiently solve the formulated NLP, we develop an ∊-bounded approximation algorithm that employs a piece-wise linear approximation approach to approximate its nonlinear term and thus reformulate the original problem as classic linear programming (LP). Particularly, we find a theoretical performance gap that is bounded by the approximation error ∊. We conduct extensive simulations to show significant performance improvement.

Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms.

In this letter, the problem of unmanned aerial vehicle (UAV) deployment, power allocation, and bandwidth allocation is investigated for a UAV-assisted wireless system operating at terahertz (THz) frequencies. In the studied model, one UAV can service ground users using the THz frequency band. However, the highly uncertain THz channel will introduce new challenges to the UAV location, user power, and bandwidth allocation optimization problems. Therefore, it is necessary to design a novel framework to deploy UAVs in the THz wireless systems. This problem is formally posed as an optimization problem whose goal is to minimize the total delays of the uplink and downlink transmissions between the UAV and the ground users by jointly optimizing the deployment of the UAV, the transmit power and the bandwidth of each user. The communication delay is crucial for emergency communications. To tackle this nonconvex delay minimization problem, an alternating algorithm is proposed while iteratively solving three subproblems: location optimization subproblem, power control subproblem, and bandwidth allocation subproblem. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 59.3%, 49.8% and 75.5% respectively compared to baseline algorithms that optimize only UAV location, bandwidth allocation or transmit power control.

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this article considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

Learning-based resilience guarantee for multi-UAV collaborative QoS management