Search Results (35)

In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments. Full article

This paper investigated the configuration of the reflecting elements for uplink non-orthogonal multiple access (NOMA) unmanned aerial vehicle (UAV)–reconfigurable intelligent surface (RIS) systems. By analyzing the practical air-to-ground (A2G) channels and phase estimation errors, a closed-form expression for the range of reflecting elements has been formulated to enhance the reliability and security of the system. Considering the energy efficiency of the system, the number of reflecting elements is optimized, aiming to maximize the energy secrecy efficiency (ESE) index under the given constraints. The simulation results verified the correctness of the derivation, which offers theoretical guidance for configuring RISs in uplink NOMA UAV systems with heterogeneous service demands. The uplink NOMA UAV system outperforms traditional terrestrial systems. The results also show that when the number of eavesdroppers increases, the influence of the number of reflecting elements on the system’s ESE becomes more significant. This demonstrates the benefits of equipping UAVs with RISs for the security of multiple eavesdropping systems. Full article

With the wide application of unmanned aerial vehicles (UAVs) in the military and civilian fields, the physical layer security of UAV-assisted communication has attracted more and more attention in recent years. Reconfigurable intelligent surface (RIS) is a revolutionizing and promising technology that can improve spectrum efficiency through intelligent reconfiguration of the propagation environment. In this paper, we investigate the physical layer security of RIS and UAV-assisted communication systems. Specifically, we consider the scenario of multiple eavesdroppers wiretapping the communication between the base station and the legitimate user and propose a secure mechanism that deploys the RIS on a dynamic UAV for security assistance. In order to maximize the average secrecy rate of the system, we propose a joint optimization problem of joint UAV flight trajectory, RIS transmit phase shift, and base station transmit power. Since the proposed problem is non-convex, it is difficult to solve it directly, so we propose a joint optimization algorithm based on block coordinate descent and successive convex optimization techniques. Simulation results verify the effectiveness of our proposed design in improving the secrecy performance of the considered system. Full article

In urban environments, the highly complex communication environment often leads to blockages in the link between ground users (GUs) and unmanned aerial vehicles (UAVs), resulting in poor communication quality. Although traditional reconfigurable intelligent surfaces (RISs) can improve wireless channel quality, they can only provide reflection services and have limited coverage. For this reason, we study a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR–RIS)–assisted UAV–mobile edge computing (UAV–MEC) network, which can serve multiple users residing in the transmission area and reflection area, and switch between reflection and transmission modes according to the relative positions of the UAV, GUs, and STAR–RIS, providing users with more flexible and efficient services. The system comprehensively considers user transmit power, time slot allocation, UAV flight trajectory, STAR–RIS mode selection, and phase angle matrix, achieving long–term energy consumpution minimization while ensuring stable task backlog queue. Since the proposed problem is a long–term stochastic optimization problem, we use the Lyapunov method to transform it into three deterministic online optimization subproblems and iteratively solve them alternately. Specifically, we firstly use the Lambert function to solve for the closed-form solution of the transmit power; then, use Lagrange duality and the Karush–Kuhn–Tucker conditions to solve time slot allocation; finally, successive convex approximation is used to obtain trajectory planning for UAVs with lower complexity, and triangular inequalities are used to solve the STAR–RIS phase shift. The simulation results show that the proposed scheme has better performance than other benchmark schemes in maintaining queue stability and reducing energy consumption. Full article

Multi-access edge computing (MEC) has emerged as a cornerstone technology for deploying 6G network services, offering efficient computation and ultra-low-latency communication. The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) further enhances wireless propagation, capacity, and coverage, presenting a transformative paradigm for next-generation networks. This paper addresses the critical challenge of task offloading and resource allocation in an MEC-based system, where a massive MIMO base station, serving multiple macro-cells, hosts the MEC server with support from a UAV-equipped RIS. We propose an optimization framework to minimize task execution latency for user equipment (UE) by jointly optimizing task offloading and communication resource allocation within this UAV-assisted, RIS-aided network. By modeling this problem as a Markov decision process (MDP) with a discrete-continuous hybrid action space, we develop a deep reinforcement learning (DRL) algorithm leveraging a hybrid space representation to solve it effectively. Extensive simulations validate the superiority of the proposed method, demonstrating significant latency reductions compared to state-of-the-art approaches, thereby advancing the feasibility of MEC in 6G networks. Full article

This study investigates security issues in a scenario involving a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted unmanned aerial vehicle (UAV) with integrated sensing and communication (ISAC) functionality (UAV-ISAC). In this scenario, both legitimate users and eavesdropping users are present, which makes security a crucial concern. Our research goal is to extend the system’s coverage and improve its flexibility through the introduction of STAR-RIS, while ensuring secure transmission rates. To achieve this, we propose a secure transmission scheme through jointly optimizing the UAV-ISAC trajectory, transmit beamforming, and the phase and amplitude adjustments of the STAR-RIS reflective elements. The approach seeks to maximize the average secrecy rate while satisfying communication and sensing performance standards and transmission security constraints. As the considered problem involves coupled variables and is non-convex, it is difficult to solve using traditional optimization methods. To address this issue, we adopt a multi-agent deep reinforcement learning (MADRL) approach, which allows agents to interact with the environment to learn optimal strategies, effectively dealing with complex environments. The simulation results demonstrate that the proposed scheme significantly enhances the system’s average secrecy rate while satisfying communication, sensing, and security constraints. Full article

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches. Full article

This paper investigates secrecy solutions for integrated sensing and communication (ISAC) systems, leveraging the combination of a reflecting intelligent surface (RIS) and an unmanned aerial vehicle (UAV) to introduce new degrees of freedom for enhanced system performance. Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. Our goal is to improve parameter estimation performance, measured by the Cramér–Rao bound (CRB), by jointly optimizing the UAV position, transmit beamforming, and RIS beamforming, subject to constraints including the UAV flight area, communication users’ quality of service (QoS) requirements, secure transmission demands, power budget, and RIS reflecting coefficient limits. To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. Our numerical results validate the effectiveness of the proposed scheme and demonstrate the potential of employing UAV-mounted RIS in ISAC systems to enhance radar sensing capabilities. Full article

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption. Full article

Reconfigurable intelligent surfaces (RISs), which consist of numerous passive reflecting elements, have emerged as a prominent technology to enhance energy and spectral efficiency for future wireless networks. RISs have the capability to intelligently reconfigure the incident wave, reflecting it towards the intended target without requiring energy for signal processing. Consequently, they have become a promising solution to support the demand for high-throughput satellite communication (SatCom) and enhanced coverage for areas inaccessible to terrestrial networks. This paper presents an asymptotic analysis of an RIS-assisted SatCom system. In this system, an unmanned aerial vehicle equipped with an RIS operates as a mobile reflector between a satellite and users. In particular, a passive beamformer is designed with the aim of asymptotically attaining optimal performance, considering the limitations imposed by practical SatCom systems. Moreover, the closed-form expressions for the ergodic achievable rate and outage probability are derived considering the proposed passive beamforming technique. Furthermore, we extend the system model to a multicast system and asymptotically analyze the optimality of the proposed scheme, leveraging the derived asymptotic results in the unicast system. The results of the simulations confirm that our analyses can precisely and analytically assess the performance of the RIS-assisted SatCom system, confirming the asymptotic optimality of the proposed scheme. Full article

Integrated sensing and communication (ISAC) is considered a key technology supporting Beyond-5G/6G (B5G/6G) networks, which allows the spectrum resources to be used for both sensing and communication. In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled ISAC scenario, where the UAV sends ISAC signals to communicate with multiple users (UEs) and senses potential targets simultaneously, and a reconfigurable intelligent surface (RIS) is deployed to enhance the communication performance. Aiming at maximizing the sum-rate throughput of the system, we formulate the joint optimization problem of the trajectory and the beamforming matrix of the UAV, the passive beamforming matrix of the RIS. Currently, many researchers are working on using deep reinforcement learning (DRL) to address such problems due to its non-convex nature; however, as the environment becomes increasingly complex, high-dimensional state space and action space lead to a decrease in the performance of DRL. To tackle this issue, we propose a novel hierarchical deep reinforcement learning (HDRL) framework to solve the optimization problem. Through decomposing the original problem into the trajectory optimization problem and the sum-rate throughput optimization problem, we adopt a hierarchical twin-delayed deep deterministic policy gradient (HTD3) structure to optimize them alternately. The experimental results demonstrate that the obtained system sum-rate throughputs of the proposed HDRL with an HTD3 structure are 33%, 50%, and 10% higher than those obtained by TD3, twin-TD3 (TTD3), and TD3 with hovering only (TD3HO), respectively. Full article

The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review of the critical role and future potential of integrating unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance Internet of Vehicles (IoV) systems within beyond-fifth-generation (B5G) and sixth-generation (6G) networks. We explore the combination of quasi-optical millimeter-wave (mmWave) signals with UAV-enabled, RIS-assisted networks and their applications in urban environments. This review covers essential areas such as channel modeling and position-aware beamforming in dynamic networks, including UAVs and IoVs. Moreover, we investigate UAV navigation and control, emphasizing the development of obstacle-free trajectory designs in dense urban areas while meeting kinodynamic and motion constraints. The emerging potential of RIS-equipped UAVs (RISeUAVs) is highlighted, along with their role in supporting IoVs and in mobile edge computing. Optimization techniques, including convex programming methods and machine learning, are explored to tackle complex challenges, with an emphasis on studying computational complexity and feasibility for real-time operations. Additionally, this review highlights the integrated localization and communication strategies to enhance UAV and autonomous ground vehicle operations. This tutorial-style overview offers insights into the technical challenges and innovative solutions of the next-generation wireless networks in smart cities, with a focus on vehicular communications. Finally, future research directions are outlined. Full article

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications face a critical security threat from impersonation attacks, where adversaries impersonate legitimate entities to infiltrate networks to obtain private data or unauthorized access. To combat such security threats, this paper proposes a novel physical layer (PHY-layer) authentication scheme for validating UAV identity in RIS-enabled UAV wireless networks. Considering that most existing works focus on traditional communication systems such as IoT and millimeter wave multiple-input multiple-output (MIMO) systems, there is currently no mature PHY-layer authentication scheme to serve RIS-UAV communication systems. To this end, our scheme leverages the unique characteristics of cascaded channels related to RIS to verify the legitimacy of UAV transmitting signals to the base station (BS). To be more precise, we first use the least squares estimate method and coordinate a descent-based algorithm to extract the cascaded channel feature. Next, we explore a quantizer to quantize the fluctuations of the channel gain that are related to the extracted channel feature. The 1-bit quantizer’s output findings are exploited to generate the authentication decision criteria, which are then tested using a binary hypothesis. The statistical signal processing technique is utilized to obtain the analytical formulations for detection and false alarm probabilities. We also conduct a computational complexity analysis of the proposed scheme. Finally, the numerical results validate the effectiveness of the proposed performance metric models and show that our detection performance can reach over 90% accuracy at a low signal-to-noise ratio (e.g., −8 dB), with a 10% improvement in detection accuracy compared with existing schemes. Full article

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system. Full article

This work focuses on maximizing the sum rate of ultra-reliable low-latency communication (URLLC) systems by leveraging unmanned aerial vehicle-mounted reconfigurable intelligent surface (UAV-RIS) to provide short packet services for users based on the non-orthogonal multiple access (NOMA) protocol. To optimize the sum rate of system, a joint optimization is performed with respect to the power allocation, UAV position, decoding order, and RIS phase shifts. As the original problem is a non-convex integer optimization problem, it is challenging to obtain the optimal solution. Therefore, approximate solutions are derived using successive convex approximation (SCA), slack variables, and penalty-based methods. The simulation results demonstrate the superiority of the proposed resource allocation algorithm compared with the benchmark algorithm with orthogonal multiple access (OMA) scheme. In addition, this work emphasizes the performance gap between the proposed communication system and the traditional Shannon communication system in terms of throughput and the performance capacity sacrificed to achieve lower latency. Full article