Non-terrestrial Network Research Articles

Reconfigurable Intelligent Surface (RIS)-Assisted Non-Terrestrial Network (NTN)-Based 6G Communications: A Contemporary Survey

This article examines the transformative potential of integrating reconfigurable intelligent surfaces (RISs) into sixth-generation (6G) wireless non-terrestrial networks (NTNs). The focus is on the RIS’s capability to address diverse user requirements, including secure data transmission, power efficiency, extended coverage, and enhanced data rates. The paper delves into the synergy between RISs and NTNs, emphasizing key components like multiple-input multiple-output (MIMO) systems and advanced radio communications. Additionally, it highlights the crucial role of artificial intelligence (AI) and machine learning (ML) in optimizing RIS-based beamforming to solve scientific and engineering challenges while ensuring energy efficiency and sustainability in NTN operations. By positioning RISs as a key enabler in shaping the future of wireless communication systems, this research underscores their significance in unlocking the full potential of NTNs and advancing next-generation wireless communications. This paper contributes valuable insights and projections for future research directions, highlighting RISs’ potential to revolutionize NTNs for 6G technologies.

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites.

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

Study to integrate Delay-Tolerant Network protocols in IoT LEO constellations for flood prevention

In the era of the Internet of Things (IoT) the development of Direct-to-Satellite IoT (DtS-IoT) applications are becoming increasingly relevant. These applications are based on enabling IoT devices to communicate directly with satellites. In this scenario, LEO satellites can provide global IoT service coverage, becoming essential to connect devices in remote areas. As an example, the deployment of NarrowBand-IoT (NB-IoT) service is being actively investigated with the apparition of Non-Terrestrial Network (NTN) and DtS-IoT concepts. Although some initiatives propose seamless and ubiquitous service, these features may not be required for IoT applications. This relaxes the requirements in the satellite constellation architecture, enabling it to be more sparse. The connection between sensors, satellites and ground segment becomes intermittent and discontinuous in these non-dense architectures. Delay/Disruptive-tolerant protocols are essential to coexist with this network characteristics. Unfortunately, traditional IoT protocols have not been designed with this delay-tolerant feature. This work tackles this challenge by integrating Delay-Tolerant Network protocols with NB-IoT architecture. Specifically, the integration is based on interconnecting the Bundle Protocol and IP traffic through a novel interface. This development has been divided in three phases: (1) The definition of an architecture and protocol stack to tackle NTN IoT scenarios with discontinuities, (2) the analysis from simulated data of the resulting protocol stack in a realistic scenario based on flooding prevention, and (3) the implementation and validation of it in a laboratory testbed. The proposed case study uses a Direct-to-Satellite IoT architecture to create an early warning flooding detection system. The simulation results provide insights of the achieved performance with these architectures when servicing hundreds of sensing nodes.

Satellite-based positioning enhanced by quantum synchronization

This study focuses on the innovative field of quantum synchronization for satellite-based navigation systems including Global Navigation Satellite Systems (GNSSs) and the Non-Terrestrial Network (NTN) component of future 6G networks integrating both communication and navigation services.By combining a four-qubit system with the theoretical approach of the Lindblad master equation, we transcend the inherent limits of standard synchronization techniques. This achievement represents a quantum leap in satellite-based positioning, highlighting the scalability and cost-effectiveness of our technique for smaller satellites. The study demonstrates the possibility of reducing synchronization errors to less than one meter, significantly improving the reliability and precision of satellite-based navigation systems.The results of this study may contribute to the future development of both user-centric localization systems (typically GNSS systems) and network-centric localization systems (typically through the NTN component of 6G networks), leading to better positioning performance, more flexible multi-functional systems with the potential to limit both cost and size of satellites.

Federated Learning in 6G Non-Terrestrial Network for IoT Services: From the Perspective of Perceptive Mobile Network

Federated Learning in 6G Non-Terrestrial Network for IoT Services: From the Perspective of Perceptive Mobile Network

ChirpPair: packet acquisition in uncoordinated access channels of Low Earth Orbit (LEO) satellite networks

Low Earth Orbit (LEO) satellite networks provide global data service coverage and has become increasingly popular. Uncoordinated access channels reduce data latency in LEO networks by allowing user terminals to transmit data packets at random times to the satellite without any coordination overhead. In this paper, packet acquisition in uncoordinated access channels of LEO networks is studied and a novel solution, called ChirpPair, is proposed, with which the satellite can detect the packets as well as estimating key parameters of the packets for data demodulation. With ChirpPair, the packet preamble consists of a chirp and its conjugate, where a chirp is a complex vector with constant magnitude and linearly increasing frequency. ChirpPair adopts a multi-stage process that gradually increases the estimation accuracy of the parameters without incurring high computation complexity. ChirpPair has been demonstrated in real-world experiments with over-the-air transmissions. ChirpPair has also been evaluated by simulations with the 3GPP New Radio (NR) Non-Terrestrial Network (NTN) channel model and the results show that ChirpPair achieves high accuracy despite its low computation complexity.

Non-Terrestrial Network Concepts in 5G and Beyond Communication Technologies

Mobile communication systems used in 5G and beyond will provide high-capacity, reliable and low-latency services within wide coverage areas. Currently, terrestrial networks are widely used, but it is expected that these networks will be replaced by hybrid network solutions where terrestrial and non-terrestrial networks operate together. These hybrid ones can perform cutting-edge complimentary solutions when the traditional networks have weak signal levels and geographical difficulties. The main motivation of this study can be stated as the increasing use of hybrid network structures for the solution of digital divine and access problems. In this study, design optimizations of hybrid wireless networks according to different system architectures and performance analyzes are given and various non-terrestrial network technologies are compared.

An auction approach to aircraft bandwidth scheduling in non-terrestrial networks

Internet Service has witnessed only limited deployment on commercial flights, where network infrastructure is lacking and subscription fees are high. It is natural to design an efficient scheduling strategy for Internet service access on flights, while maximizing social welfare. There are challenges to use either antennas on the ground or geostationary (GEO) satellites in space as Internet Service Provider (ISP) equipment. First, preemptive-purchase and resource-allocation in post-paid plans may not satisfy the dynamic demand for large amounts of flight-ISP machine links. Second, finding an optimal, dynamic, online solution based on flight demands is NP-hard. Third, existing solutions often fail to maximize either seller profit or social welfare. To address the aforementioned challenges, we formulate an online bandwidth scheduling integer linear program (ILP) among flights, antennas, and GEO satellites in a Non-Terrestrial Network (NTN). We transform the ILP into an exponential optimization problem, which is then relaxed, with its dual formulated. We iteratively solve a series of dual subproblems in polynomial time, towards social welfare maximization with a good competitive ratio in typical network settings. In empirical studies, our algorithm achieved a much better competitive ratio than the theoretical worst-case guarantee, and clearly outperforms alternative algorithms.

Geometrical design method of Walker constellation in non-terrestrial network

To meet the requirement of mega low earth orbit (LEO) constellation design in non-terrestrial network (NTN), a geometric configuration design method of Walker constellation applicable to arbitrary latitude range coverage multiple and satellite quantity scale is proposed. First, a Walker configuration representation based on the streets of coverage (SoC) characteristic parameters is proposed. Then, satellite quantity required for coverage and the SoC parameters are estimated by calculating the width and adjacent spacing of SoC, which can improve the enumeration efficiency. Finally, by adjusting the phase and quantity of the satellite on the SoC, the constellation configuration with optimal comprehensive performances is selected from multiple groups of results. To demonstrate the effectiveness of the proposed design method, the performance comparisons among the existing mega LEO constellation systems are conducted. Compared with the existing configurations, the configuration calculated by the proposed method can efficiently reduce the satellite quantity required for coverage by about 20%, and can also satisfy the comprehensive demands including coverage performance, inter-satellite link, configuration redundancy and collision avoidance by reasonably selecting configuration parameters.

Outage Performance of RIS-Assisted Cognitive Non-Terrestrial Network With NOMA

Outage Performance of RIS-Assisted Cognitive Non-Terrestrial Network With NOMA

Integrated Network Control Architecture for Terrestrial and Non-Terrestrial Network Convergence

Integrated Network Control Architecture for Terrestrial and Non-Terrestrial Network Convergence

Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios

In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

Feasibility and Opportunities of Terrestrial Network and Non-Terrestrial Network Spectrum Sharing

Feasibility and Opportunities of Terrestrial Network and Non-Terrestrial Network Spectrum Sharing

HAPS in the Non-Terrestrial Network Nexus: Prospective Architectures and Performance Insights

HAPS in the Non-Terrestrial Network Nexus: Prospective Architectures and Performance Insights

Energy-Efficient Service-Aware Multi-Connectivity Scheduler for Uplink Multi-Layer Non-Terrestrial Networks

This paper introduces the concept of energy efficiency (EE) in the uplink with the capability of multi-connectivity (MC) in a multi-orbit non-terrestrial network (NTN), where user terminals (UTs) can be simultaneously served by more than one satellite to achieve higher peak throughput at reduced energy consumption. This concept also considers the service classification of the users, so that network dimensioning is performed in order to satisfy the quality of service (QoS) requirement of users. MC can increase throughput, but this entails increased power consumption at user terminal for uplink transmissions. To this end, an energy-efficient service-aware multi-connectivity (EE-SAMC) scheduling algorithm is developed in this paper to improve the EE of uplink communications. EE-SAMC uses available radio resources and propagation information to intelligently define a dynamic resource allocation pattern, that optimally routes traffic so as to reduce the energy consumption at the UT while ensuring QoS is maximized. EE-SAMC is designed based on the formulation of a non-convex combinatorial problem, it is solved in two ways involving firstly an optimization solution and secondly a heuristic approach. The effectiveness of EE-SAMC is compared with random allocation, round robin and heuristic schedulers in terms of EE, throughput and delay; EE-SAMC outperforms all schedulers.

Handover Triggering Prediction with the Two-Step XGBOOST Ensemble Algorithm for Conditional Handover in Non-Terrestrial Networks

A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth’s orbit at a constant speed. Ground services from continuously moving satellites cause frequent handovers. In addition, frequent handovers may come as a load between User Equipment (UE) and the communication system, which leads to degradation of service quality. Unlike Terrestrial Networks (TN), communication services are provided to UEs at altitudes ranging from 20 km to 35,584 km, rather than from base stations close to the ground. Service at high altitudes is unreliable due to the measurement values that were previously used as quality indicators to operate terrestrial networks. Moreover, service at high altitudes demands long-distance communication, and propagation delay occurs from the long-distance communication. In the 3GPP Rel. 17 document, it is suggested that the above problems should be solved. This paper tries to solve the problem by proposing the two-step XGBOOST, a CART-based Gradient Boosting Model. Handover in TN uses measurement-based conditional handover (CHO), but the measured values in the NTN environment are not valid. Using this, the distance between the UE and the center of the cell and the elevation angle are used to construct a model that predicts the HO triggering time point. In order to overcome the propagation delay caused by communication at a high altitude, a model that predicts the distance and elevation angle between the UE and the center of the cell considering the propagation delay is proposed. The model is composed of two-step XGBOOST. The one-step model is a model in which the UE predicts the distance and elevation angle between cell centers after propagation delay at the time when satellite position information is transmitted to the UE. The two-step model predicts handover triggering occurrence based on the data predicted by the one-step result. As a result of the experiment, the model considering the propagation delay showed about 8% better performance on average than the model not considering the propagation delay, and the XGBOOST model achieved an average F1-score of 0.9891 in the propagation delay experiments.

Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems

This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Inmarsat. This study simulates an NTN satellite network with typical characteristics defined by 3GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites.

Multi-layer Non-terrestrial Network for Beyond 5G/6G Mobile Communications

Multi-layer Non-terrestrial Network for Beyond 5G/6G Mobile Communications

Multi-agent deep reinforcement learning for user association and resource allocation in integrated terrestrial and non-terrestrial networks

Integrating the terrestrial network with non-terrestrial networks to provide radio access as anticipated in the beyond 5G networks calls for efficient user association and resource allocation strategies. In this work, a weighted sum single objective optimization problem that maximizes the total network data rate while minimizing the mobility-induced handoffs and prioritizing service provisioning of mission-critical users in the integrated terrestrial and non-terrestrial network is formulated. The problem’s complexity is reduced by solving the defined problem in two phases; the user association followed by the resource distribution phase. Several proposed approaches to the user association sub-problem utilize a central node that requires nearly-complete information, which may not be available in real time. On the contrary, this paper proposes a centralized training and distributed execution multi-agent dueling double deep Q network (MA3DQN) solution. Each user collects the channel state and access node loading information in this approach and makes an association decision that considers its quality of service requirements. The algorithm’s performance is validated through comparison with the genetic algorithm (GA), the integer linear programming (ILP), a heuristic approximation-based solution, the greedy approach, and the random user association (RUA) algorithm. Moreover, the paper simulates the multi-agent deep Q network solution as an additional benchmark algorithm. Simulation results reveal that as the number of users in the network increases, the acquired data rate of the MA3DQN is within 0.48% and 0.4% of that achieved by the GA and ILP, respectively, and outperforms all other algorithms. Notably, the proposed MA3DQN algorithm presents the best running time, attaining a gain of 99.9% over the GA algorithm, which performs the poorest among the algorithms characterized by polynomial worst-case time complexity. Besides, the MA3DQN approach maintains a handoff probability of zero, unlike the ILP, the approximation-based, greedy, and RUA solutions.