Search Results (43)

To further enhance the stability and security of image transmission in FSO (Free Space Optics) aviation platforms, this paper proposes a communication transmission scheme that integrates a chaotic image encryption system with the HAP (high-altitude platform) environment. This scheme effectively combines the chaotic image encryption algorithm with the atmospheric turbulence channel transmission process, improving the anti-interference capabilities and security of HAP optical communication for image transmission. First, a five-dimensional hyperchaotic system with complex dynamic characteristics is introduced, and the system’s chaotic behaviors and dynamic properties are explored. The improved system model incorporates chaotic mapping and DNA coding techniques, forming a robust chaotic image encryption system, whose performance is experimentally validated. Next, the feasibility of integrating the chaotic image encryption system with HAP optical communication is discussed. A detailed description of the corresponding turbulence model and test conditions is provided. To verify the scheme’s feasibility, plaintext images of varying sizes are selected for experiments, comparing the transmission performance of both unencrypted and encrypted images under three turbulence levels: weak, medium, and strong. The impact on image communication quality is quantitatively analyzed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index measure). Finally, the effect of malicious interception attacks, caused by noise interference from different levels of atmospheric turbulence, is examined. The robustness and feasibility of the proposed scheme are validated, providing a promising approach for integrating HAP optical communication’s anti-turbulence capabilities with chaotic image encryption. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

As urban populations expand and congestion intensifies, traditional ground transportation struggles to satisfy escalating mobility demands. Unmanned Electric Vertical Take-Off and Landing (eVTOL) aircraft, as a key enabler of Urban Air Mobility (UAM), leverage low-altitude airspace to alleviate ground traffic while offering environmentally sustainable solutions. However, supporting high bandwidth, real-time video applications, such as Virtual Reality (VR), Augmented Reality (AR), and 360° streaming, remains a major challenge, particularly within bandwidth-constrained metropolitan regions. This study proposes a novel Unmanned Aerial Vehicle (UAV)-enabled video streaming architecture that integrates 6G wireless technologies with intelligent routing strategies across cooperative airborne nodes, including unmanned eVTOLs and High-Altitude Platform Systems (HAPS). By relaying video data from low-congestion ground base stations to high-demand urban zones via autonomous aerial relays, the proposed system enhances spectrum utilization and improves streaming stability. Simulation results validate the framework’s capability to support immersive media applications in next-generation autonomous air mobility systems, aligning with the vision of scalable, resilient 3D transportation infrastructure. Full article

As sensor technology integrates into modern life, diverse sensing devices have become essential for collecting critical data that enables human–machine interfaces such as autonomous vehicles and healthcare monitoring systems. However, the growing number of sensor devices places significant demands on network capacity, which is constrained by the limitations of radio frequency (RF) technology. RF-based communication faces challenges such as bandwidth congestion and interference in densely populated areas. To overcome these challenges, a combination of RF with free-space optical (FSO) communication is presented. FSO is a laser-based wireless solution that offers high data rates and secure communication, similar to fiber optics but without the need for physical cables. However, FSO is highly susceptible to atmospheric turbulence and conditions such as fog and smoke, which can degrade performance. By combining the strengths of both RF and FSO, a hybrid FSO/RF system can enhance network reliability, ensuring seamless communication in dynamic urban environments. This review examines hybrid FSO/RF systems, covering both theoretical models and real-world applications. Three categories of hybrid systems, namely hard switching, soft switching, and relay-based mechanisms, are proposed, with graphical models provided to improve understanding. In addition, multi-platform applications, including autonomous, unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, are presented. Finally, the paper identifies key challenges and outlines future research directions for hybrid communication networks. Full article

Semantic communication technology, as it allows for source data meaning extraction and the transmission of appropriate semantic information only, has the potential to extend Shannon’s paradigm, which is concerned with the reproduction of a message from one location to another, regardless of its meaning. Nevertheless, some user terminals (UTs) may experience inadequate service due to their geolocation in reference to the base stations, which may entirely affect the accuracy of transmission and complicate deployment and implementation. A High-Altitude Platform Station (HAPS) serves as a key enabler for the deployment of wireless broadband in inaccessible areas, such as in coastal, desert, and mountainous areas. This paper proposes a novel HAPS relay-based semantic communication scheme, named DeepSTAS, which leverages deep learning techniques to enhance transmission accuracy. The proposed scheme focuses on attention-based semantic signal decoding, denoising, and forwarding modes; thus, called a CSA-DCGAN SDF HAPS relay network. The simulation results reveal that the proposed system with attention mechanisms significantly outperforms the system without attention mechanisms, both in peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index (MS-SSIM); the proposed system can achieve a 2 dB gain when leveraging the attention mechanisms, and a PSNR of 38.5 dB can be obtained, with an MS-SSIM exceeding 0.999 at an approximate SNR of only 20 dB. The system provides considerable performance, more than 37 dB, and a corresponding MS-SSIM close to 0.999 at an estimated SNR of 20 dB when the CIFAR-100 dataset is considered and an MS-SSIM of 0.965 at an approximate SNR of only 10 dB on the Kodak dataset. The proposed system holds promise to maintain consistent performance even at low SNRs across various channel conditions. Full article

In Japan, the DEURAS system has been deployed to detect and locate illegal radio sources that either exceed permissible transmission power limits or operate on unauthorized frequencies. This system utilizes receiving antennas installed on high-rise buildings and radio towers to capture radio signals and estimate the location of the transmission source. However, in densely built urban environments, the accuracy of location estimation is often compromised due to signal reflections and diffractions. Additionally, in large-scale disasters such as earthquakes, terrestrial infrastructure may be severely damaged, making it essential to develop a localization system that operates independently of ground-based stations. To overcome these limitations, this study proposes a localization system based on a high-altitude-platform station (HAPS), which operates at an altitude of approximately 20 km. The feasibility and effectiveness of the proposed system are evaluated through numerical simulations, considering various environmental conditions. The results demonstrate that HAPS-based localization significantly improves positioning accuracy, offering a robust and high-precision alternative for radio source detection, particularly in scenarios where traditional ground-based systems are unreliable or unavailable. Full article

The Italian Aerospace Research Centre (CIRA) is developing an unmanned stratospheric platform for Earth observation and telecommunications, known as a High-Altitude Pseudo-Satellite (HAPS). This paper presents an aerodynamic and stability analysis of a new closed-wing HAPS configuration. The design uses a hybrid approach, combining aerodynamic and aerostatic forces to achieve weight balance, with the stability analysis accounting for the buoyancy force applied at the center of volume of the structure. Following the initial design phase, which aims for an altitude of 20 km, a speed of 16 m/s, and a payload capacity of 20 kg, a suitable configuration using a NACA 0018 airfoil is selected. The aircraft lift–drag curve is evaluated using a stationary, incompressible Reynolds-Averaged Navier–Stokes (RANS) analysis with a k-$\omega$ SST turbulence model in OpenFoam. A detailed longitudinal and lateral-directional stability analysis is also conducted using OpenFOam and AVL software. Full article

This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted downlink hybrid free-space optics (FSO)/radio frequency (RF) space–air–ground integrated network (SAGIN) architecture, where the high altitude platform (HAP) converts the optical signal sent by the satellite into an electrical signal through optoelectronic conversion. The drone equipped with RIS dynamically adjusts the signal path to serve ground users, thereby addressing communication challenges caused by RF link blockages from clouds or buildings. To improve the security performance of SAGIN, this paper maximizes the sum secrecy rate (SSR) by optimizing the power allocation, RIS phase shift, and drone trajectory. Then, an alternating iterative framework is proposed for a joint solution using the simulated annealing algorithm, semi-definite programming, and the designed deep deterministic policy gradient (DDPG) algorithm. The simulation results show that the proposed scheme can significantly enhance security performance. Specifically, compared with the NOMA and SDMA schemes, the SSR of the proposed scheme is increased by 39.7% and 286.7%, respectively. Full article

In this article, we propose a cell-free network architecture for an unmanned aerial vehicle (UAV) base station (BS), i.e., UBS, incorporating high-altitude platform stations (HAPSs) as central processing units (CPUs). The goal is to guarantee the quality of service (QoS) of user equipment (UE), reduce energy consumption, extend communication time, and facilitate rescue operations. The millimeter-wave (mmWave) frequency band is deployed in access and backhaul links to satisfy UE QoS requirements and high backhaul demands. The proposed framework jointly optimizes user association, backhaul bandwidth allocation, and power allocation to maximize energy efficiency while meeting QoS requirements. The optimization problem, modeled as non-convex mixed-integer nonlinear fractional programming, is solved through a three-stage iterative algorithm. This includes (1) optimizing power allocation based on Dinkelbach transformation and a successive convex approximation (SCA) method, (2) clustering UBSs using the Lagrangian method, and (3) deriving a closed-form bandwidth allocation factor. The proposed algorithm significantly outperforms many traditional algorithms in performance while maintaining low computational complexity. Full article

Satellite–aerial–ground integrated networks (SAGINs) combined with hybrid free-space optical (FSO) and radio frequency (RF) transmissions have shown great potential in improving service throughput and reliability. The coverage mismatch and rate limitation of traditional hybrid FSO/RF design have restricted its development. In this paper, we investigate the performance of parallel FSO/RF transmissions in SAGIN, taking into account the effect of weather conditions and the quality of service (QoS) of ground users. A three-hop relay system is proposed, where the FSO and RF links jointly provide services to ground users in remote areas. Specifically, considering the limited transmit power of the relay node, we have studied the optimal power allocation between parallel FSO and RF links to further improve system energy efficiency. The performance of the proposed system is evaluated in terms of capacity outage probability, weighted average bit-error rate (BER), and energy efficiency. Moreover, asymptotic capacity outage probability is also derived to obtain more engineering insights. Finally, numerical results show that the energy efficiency of the proposed parallel scheme improves by 30.9% compared to only the FSO scheme at a total transmit power of 15 dBW. Full article

Currently, there is a system in Japan to detect illegal radio transmitting sources, known as the DEURAS system. Even though crackdowns on illegal radio stations are conducted on a regular basis every year, the number of illegal emission cases still tends to increase, as ordinary citizens are now able to handle advanced wireless communication technologies, e.g., via software-defined radio. However, the current surveillance system may not be able to accurately detect the source in areas where large buildings are densely packed, such as urban areas, due to the effects of reflected waves. Therefore, in this study, we proposed a system for estimating the location of the source of transmission using a high-flying unmanned aerial vehicle called HAPS. The simulation results using numerical analysis software show that the proposed system can estimate the location of the source over a wider area and with higher accuracy than conventional monitoring systems. Full article

High-altitude platform (HAP) drones and satellites collaborate to form a network that provides edge computing services to terrestrial internet of things (IoT) devices, which is considered a promising method. In this network, IoT devices’ tasks can be split into multiple parts and processed by servers at non-terrestrial nodes in different locations, thereby reducing task processing delays. However, splitting tasks and allocating communication and computing resources are important challenges. In this paper, we investigate the task offloading and resource allocation problem in multi-HAP drones and multi-satellite collaborative networks. In particular, we formulate a task splitting and communication and computing resource optimization problem to minimize the total delay of all IoT devices’ tasks. To solve this problem, we first transform and decompose the original problem into two subproblems. We design a task splitting optimization algorithm based on deep reinforcement learning, which can achieve online task offloading decision-making. This algorithm structurally designs the actor network to ensure that output actions are always valid. Furthermore, we utilize convex optimization methods to optimize the resource allocation subproblem. The simulation results show that our algorithm can effectively converge and significantly reduce the total task processing delay when compared with other baseline algorithms. Full article

The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach. Full article

Recently, there has been an increase in interest in using optical wireless communication (OWC) links on high-altitude platforms (HAPs) for satellite applications. We implement an orbital angular momentum (OAM) multiplexed orthogonal frequency division multiplexing (OFDM) system using an OWC link. A space-to-air scenario is considered in which transmission pointing errors, geometric loss, turbulence, and additional link losses are taken into account to extend the transmission range, system capacity, and throughput. At 200 Gbps per channel data rate, four different OAM modes are implemented with higher order Laguerre–Gaussian (LG) modes of [0,0], [0,13], [0,40], and [0,80]. An aggregate 800 Gbps data rate can deliver a maximum OWC range of 3300–5000 km for all channels. The maximum received power over the 1000 km range is −19.34 to −32.59 dBm with an additional gain of 0–2.5 dB. It is also possible to obtain a better performance over large distances of 500–3500 km with an error vector magnitude of 2.98–17.5%. Furthermore, a high gain of −40.80 dB, a signal-to-noise ratio (SNR) of 55.21 dB, and an optical SNR of 67.25 dB can be achieved for varied transmitter pointing errors of 0.1 rad. As compared to other literature, this system exhibits a superior performance. Full article

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources. Full article