High Altitude Platform Station Research Articles

Multiple HAPS-based space-air-ground network with FSO communication: a performance analysis.

Due to the fact that the existing generation of wireless communication cannot possibly keep up with the current traffic explosion and emerging applications, research and development on next-generation (i.e.,sixth generation, 6G) wireless technologies is being carried out worldwide. In this regard, it is anticipated that the space-air-ground (SAG) network with free space optics (FSO) communication can provide the terabits per second throughput necessary to sustain various potential 6G applications. However, FSO communications are susceptible to atmospheric turbulence, pointing errors, and beam scintillation effects. To remedy the severe atmospheric effects, we propose a multiple high-altitude platform station (HAPS)-based SAG network with a HAPS selection scheme. For the proposed system, we have derived the closed-form expressions for outage probability, average symbol error rate (SER), ergodic capacity, and outage capacity over Málaga distribution with pointing errors. Further, the asymptotic expressions for outage probability, average SER, and outage capacity were derived to enhance the comprehension of the system from a practical standpoint. It is observed from the numerical results that the multiple HAPS-based FSO system performs better than the existing HAPS-based FSO systems.

Energy Sustainability in Dense Radio Access Networks via High Altitude Platform Stations

Energy Sustainability in Dense Radio Access Networks via High Altitude Platform Stations

Hybrid FSO/RF Communications in Space–Air–Ground Integrated Networks: A Reduced Overhead Link Selection Policy

Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.

FLSTRA: Federated Learning in Stratosphere

We propose a federated learning (FL) in stratosphere (FLSTRA) system,where a high altitude platformstation (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithmto achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problemis non-convex; thus, we propose an iterative algorithmto solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)–Assisted Vehicular Networks

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks

The deployment of non-terrestrial networks (NTNs) is envisioned to achieve global coverage for 6G and beyond. In addition to space nodes, aerial NTN nodes such as high-altitude platform stations (HAPSs) and rotary-wing unmanned aerial vehicles (UAVs) could be deployed, based on the intended coverage and operational altitude requirements. NTN nodes have the potential to support both wireless access and backhauling. While the onboard base station provides wireless access for the end users, the backhauling link connects the airborne/space-borne base station to the core network. With its high data transmission capability comparable to fiber optics and its ability to operate in the interference-free optical spectrum, free space optical (FSO) communication is ideally suited to backhauling requirements in NTNs. In this paper, we present a comprehensive tutorial on airborne FSO backhauling. We first delve into the fundamentals of FSO signal transmission and discuss aspects such as geometrical loss, atmospheric attenuation, turbulence-induced fading, and pointing errors, all of which are critical for determining received signal levels and related link budget calculations. Then, we discuss the requirements of airborne backhaul system architectures, based on use cases. While single-layer backhaul systems are sufficient for providing coverage in rural areas, multi-layer designs are typically required to establish connectivity in urban areas, where line of sight (LoS) links are harder to maintain. We review physical layer design principles for FSO-based airborne links, discussing both intensity modulation/direct detection (IM/DD) and coherent modulation/coherent demodulation (CM/CD). Another critical design criteria for airborne backhauling is self-sustainability, which is further discussed in our paper. We conclude the paper by discussing current challenges and future research directions. In this context, we discuss reconfigurable intelligent surfaces (RIS) and spatial division multiplexing (SDM), for improved performance and an extended transmission range. We emphasize the importance of advanced handover techniques and scalability issues for practical implementation. We also highlight the growing role of artificial intelligence/machine learning (AI/ML) and their potential applications in the design and optimization of future FSO-based NTNs.

Multimode High-Altitude Platform Stations for Next-Generation Wireless Networks: Selection Mechanism, Benefits, and Potential Challenges

Multimode High-Altitude Platform Stations for Next-Generation Wireless Networks: Selection Mechanism, Benefits, and Potential Challenges

Physical Layer Security of HAPS-Based Space–Air–Ground-Integrated Network With Hybrid FSO/RF Communication

In this paper, we study the physical layer security performance of a downlink space-air-ground integrated network (SAGIN), where a satellite communicates with the ground user/destination via a high-altitude platform station (HAPS)- based relay in the presence of an eavesdropper. In particular, we propose to use hybrid free space optical (FSO)/radio frequency (RF) transmission to enhance the security of the considered system. Herein, the FSO and RF links are assumed to experience Gamma-Gamma and Shadowed-Rician distributions, respectively. Specifically, we derive exact and asymptotic expressions of secrecy outage probability for the considered SAGIN by employing intensity modulation/direct detection. We provide the numerical and simulation results to validate our analysis.

Legal Issues Pertaining to High Altitude Platform Station Implementation in Indonesia as an Archipelagic State

Information is a necessity for the interests of government, the economy, social culture, as well as defense and security in the era of industrial revolution 4.0. Since 1976, the Indonesian government has been working on developing a satellite communication system starting with the Palapa A1 Satellite, followed by its subsequent generations. However, this system has limitations, particularly in providing coverage to rural areas in Indonesia. To address this issue, the government has started the procurement of the Wahana Dirgantara Super or High-Altitude Platform Station (HAPS) in Indonesia. HAPS are stations positioned at altitudes of 20 to 50 km above the Earth's surface. Given Indonesia's geographical location, HAPS is seen as the most suitable solution as it utilizes non-ground terrestrial technology to improve information and communication technology coverage in rural areas. However, the location on which the HAPS is positioned might trigger new issues, particularly with regard to state sovereignty and disruption of flight traffic. The operation of HAPS also needs further consideration so that the protection of personal data in Indonesia will not be disrupted. This article employs normative juridical research as a methodology. The research collected and analyzed primary sources in the form of law, regulation, and policy at the national and regional levels, as well as secondary sources that are available in textbooks, journal articles, and website content. This article aims to explain the development of HAPS regulations based on international law and national law and to examine the legal issues related to the procurement of HAPS in Indonesia . At the end, this article suggests that it is necessary to regulate HAPS in Indonesia with reference to aviation safety, security, and liability issues as well as maintaining state sovereignty and ensuring personal data protection.

Sustaining Dynamic Traffic in Dense Urban Areas with High Altitude Platform Stations (HAPS)

Sustaining Dynamic Traffic in Dense Urban Areas with High Altitude Platform Stations (HAPS)

Trajectory Optimization for FSO Based U-IoT Backhaul Networks

Considering many advancements in aerial platform technology, aerial backhaul networks (ABNs) are expected to play a big role in 6th generation (6G) mobile systems. Considering the significant demand in capacity of Internet of Things (IoT) networks, ABNs using free space optical (FSO) signals are receiving attention as an alternative to using traditional radio frequency (RF) signals. However, FSO based ABNs face the challenge of needing to avoid obstacles (e.g., clouds) in the vertical channel to maintain line-of-sight (LoS) between the ground terminals (GTs) and the unmanned aerial vehicle (UAV) based high-altitude platform stations (HAPSs). Therefore, in this paper, a trajectory optimized FSO based UAV-IoT (U-IoT) backhaul network (TOFU) scheme that minimizes the end-to-end outage probability is proposed. Considering moving clouds and obstacles, the TOFU scheme identifies the changing LoS area and applies gradient descent based on optimized control of the HAP and relay UAVs to minimize the end-to-end outage probability. Simulation experiments were conducted to include various cloud properties and orientations, where the results show that the TOFU scheme can provide significant improvements in the end-to-end outage probability and throughput compared to other benchmarked schemes.

Resource-Efficient HAPS-RIS Enabled Beyond-Cell Communications

In the future, urban regions will encounter a massive number of capacity-hungry devices. Relying solely on terrestrial networks for serving all UEs will be a cost-ineffective approach. Consequently, with the anticipated supply and demand mismatch, several UEs will be unsupported. To offer service to the left-out UEs, we employ an energy-efficient and cost-effective beyond-cell communications approach, which uses reconfigurable intelligent surfaces (RIS) on a high-altitude platform station (HAPS). Particularly, unsupported UEs will be connected to a dedicated control station (CS) through RIS-mounted HAPS. A novel resource-efficient optimization problem is formulated that maximizes the number of connected UEs, while minimizing the total power consumed by the CS and RIS. Since the resulting problem is a mixed-integer nonlinear program (MINLP), a low-complexity two-stage algorithm is developed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark approach in terms of the percentage of connected UEs and the resource-efficiency (RE). Also, the results show that the number of connected UEs is more sensitive to transmit power at the CS than the HAPS size.

Design and Evaluation on Onboard Antenna Pointing Control System for a Wireless Relay System Using Fixed-Wing UAV

Among several usages of unmanned aerial vehicles (UAV), wireless relay systems for high altitudes using fixed-wing UAVs, or high-altitude platform stations (HAPS), are some of the most promising applications. To realize the systems by making the most of advantages of the long flight duration and endurance of fixed-wing airplanes, this paper proposes an antenna pointing control system using mechanical gimbals onboard a fixed-wing UAV continuously turning midair and describes results of the blocking analysis of the antenna driving angles of the gimbal directed to a ground station, the design of the antenna pointing control system, and the evaluation of its performance. It is confirmed by the evaluation that, though the antenna pointing control accuracy is greatly influenced by the noisy antenna pointing direction command, its accuracy is greatly improved by using the highly accurate RF sensor to detect antenna pointing direction.

User Selection and Codebook Design for NOMA-Based High Altitude Platform Station (HAPS) Communications

High altitude platform station (HAPS) communications have made a tremendous impact on recent research into sixth-generation (6G) and beyond wireless networks. The large coverage area and significant computational capability of HAPS systems enable many areas of utilization in 6G and beyond applications, including Internet of Things (IoT) services, augmented reality, and connected autonomous vehicles. In addition, non-orthogonal multiple access (NOMA) is a cutting-edge technology that can be utilized to enhance spectral efficiency in HAPS systems. In this paper, we exploit NOMA-based HAPS communications and multiple antennas to meet the connectivity, reliability, and high-data-rate requirements of 6G and beyond applications. We propose a user selection and correlation-based user pairing algorithm for a NOMA-based multi-user HAPS system. Moreover, we investigate the codebook design for HAPS communication and adapt the polar-cap codebook (PCC) to the HAPS channel which shows Rician fading propagation characteristics dominated by the line-of-sight (LOS) component. Performance evaluations show that the proposed user selection algorithm is perfectly suited to the HAPS channel and that the PCC provides a remarkable spectral efficiency.

High-precision dynamic pointing method for improving the acquisition performance of laser communication between high-altitude platform stations

Fast and stable acquisition is the prerequisite for establishing a space laser communication links. In this paper, the acquisition technology of establishing a space laser communication link between high-altitude platform stations (HAPS) is studied, a dynamic pointing and scanning model is established, and the acquisition performance of the laser communication system under line of sight (LOS) disturbance is analyzed based on this model. To improve the acquisition performance, we propose a high-precision composite pointing method to reduce the pointing error of the line of sight. A composite pointing controller with a fast steering mirror (FSM) is designed to reduce pointing errors, and the aiming accuracy of line of sight is improved by predicting the target with improved unscented Kalman filter (IUKF). A dynamic laser communication experiment based on HAPS is built to verify the method. The results show that the proposed method reduces the initial pointing error by 45%, and the stability of the line of sight is increased by a factor of 5. By conducting several acquisition tests, the average acquisition time is 1.57 s with 100% acquisition probability. The acquisition performance is significantly improved compared with that before the improvement, in which the acquisition probability is increased by 8% and the acquisition time is reduced by 44.3%, which verifies the effectiveness of the proposed method and lays the foundation for the establishment of a fast communication link for laser communication between HAPS.

HAPS-Assisted Hybrid RF-FSO Multicast Communications: Error and Outage Analysis

In this article, we study the performance of multiple-hop mixed radio frequency (RF)/free-space optical (FSO) communication-based decode-and-forward protocol for multicast networks. So far, serving a large number of users is considered a promising approach for real-time applications to address the massive data traffic demands. In this regard, we propose two practical use cases. In the former model, we propose a high altitude platform station (HAPS)-aided mixed RF/FSO/RF communication scheme where a terrestrial ground station intends to communicate with a cluster of nodes through two stratospheric HAPS systems. In the latter model, we assume that the line of sight connectivity is inaccessible between the two HAPS systems due to high attenuation caused by large propagation distances. Thereby, we propose a low Earth orbit satellite-aided mixed RF/FSO/FSO/RF communication. For the proposed scenarios, closed-form expressions of outage probability (OP) and bit error rate are derived. In addition, to illustrate the asymptotic behavior of the proposed models, diversity gains are obtained. Furthermore, ergodic capacity and energy efficiency (EE) are provided for both scenarios. Finally, the simulation results are provided to validate the theoretical derivations. The results show that a satellite-aided mixed RF/FSO/FSO/RF scenario achieves better OP, whereas an HAPS-aided mixed RF/FSO/RF scenario can achieve a higher EE.

Machine Learning Techniques for Non-Terrestrial Networks

Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements and integration in the 5G 3GPP standards, NTNs have been gaining importance in the last years and will provide further applications and services. 3GPP standardization is integrating low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs) and unmanned aerial systems (UASs) as non-terrestrial elements (NTEs) in the NTNs within the terrestrial 5G standard. Considering the NTE characteristics (e.g., traffic congestion, processing capacity, oscillation, altitude, pitch), it is difficult to dynamically set the optimal connection based also on the required service to properly steer the antenna beam or to schedule the UE. To this aim, machine learning (ML) can be helpful. In this paper, we present novel services supported by the NTNs and their architectures for the integration in the terrestrial 5G 3GPP standards. Then, ML techniques are proposed for managing NTN connectivity as well as to improve service performance.

Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

High Altitude Platform Stations Aided Cloud-Computing Solution for Rural-Environment IoT Applications

This work proposes a flexible and scalable Smart Rural (SR) system for gathering and processing IoT data from remote rural areas with no traditional communication coverage as a handicap. We offer an architecture structured in distinct segments using emerging technologies such as IoT, 5G, Cloud and High Altitude Platform Station (HAPS). This proposal is applied to the rural environment to cover thus all the needs of the system in the collection of IoT data from these remote rural areas, its coverage by space vehicles and its processing and storage through 5G terrestrial networks and cloud services. The proposal includes the deployment of IoT sensors and the development of Amazon Web Services (AWS). On the other hand, the part of the space segment considered by HAPS has been simulated for different space channels. This way offers a complete and automated SR system that allows access to these IoT data from remote rural areas through the Internet.

Analysis of a HAPS-Aided GNSS in Urban Areas Using a RAIM Algorithm

The global averaged civilian positioning accuracy is still at meter level for all existing Global Navigation Satellite Systems (GNSSs), and the performance is even worse in urban areas. At lower altitudes than satellites, high altitude platform stations (HAPS) offer several benefits, such as lower latency, less pathloss, and likely smaller overall estimation error for the parameters associated in the pseudorange equation. HAPS can support GNSSs in many ways, and in this paper we treat the HAPS as another type of ranging source. In so doing, we examine the positioning performance of a HAPS-aided GPS system in an urban area using both a simulation and physical experiment. The HAPS measurements are unavailable today; therefore, they are modeled in a rather simple but logical manner in both the simulation and physical experiment. We show that the HAPS can improve the horizontal dilution of precision (HDOP), the vertical dilution of precision (VDOP), and the 3D positioning accuracy of GPS in both suburban and dense urban areas. We also demonstrate the applicability of a RAIM algorithm for the HAPS-aided GPS system, especially in the dense urban area.