Holistic Service-Based Architecture for Space-Air-Ground Integrated Network for 5G-Advanced and Beyond

The Service-based Architecture(SBA) is one of the key innovations of 5G architecture that leverage modularized, self-contained and independent services to provide flexible and cloud-native 5G network. In this paper, SBA for Space-Air-Ground Integrated Network(SAGIN) is investigated to enable the 5G integration deployment. This paper proposes a novel Holistic Service-based Architecture(H-SBA)for SAGIN of 5G-Advanced and beyond, i.e., 6G. The H-SBA introduces the concept of end-to-end servicebased architecture design. The "Network Function Service", introduced in 5G SBA, is extended from Control Plane to User Plane, from core network to access network. Based on H-SBA, the new generation of protocol design is proposed, which proposes to use IETF QUIC and SRv6 to substitute 5G HTTP/2.0 and GTP-U. Testing results show that new protocols can achieve low latency and high throughput, making them promising candidate for H-SBA.

Service Customized Space-Air-Ground Integrated Network for Immersive Media: Architecture, Key Technologies, and Prospects

The space-air-ground integrated network(SAGIN) is regarded as the key approach to realize global coverage in future network and it reaches broad access for various services. Being the new paradigm of service, immersive media(IM) has attracted users’ attention for its virtualization, but it poses challenges to network performance, e.g. bandwidth, rate, latency. However, the SAGIN has limitations in supporting IM services, such as 4 K/8 K video, virtual reality, and interactive games. In this paper, a novel service customized SAGIN architecture for IM applications(SAG-IM) is proposed, which achieves content interactive and real-time communication among terminal users. State-of-the-art research is investigated in detail to facilitate the combination of SAGIN and service customized technology, which provides endto-end differentiated services for users. Besides, the functional components of SAG-IM contain the infrastructure layer, perception layer, intelligence layer, and application layer, reaching the capabilities of intelligent management of the network. Moreover, to provide IM content with ultra-high-definition and high frame rate for the optimal user experience, the promising key technologies on intelligent routing and delivery are discussed. The performance evaluation shows the superiority of SAG-IM in supporting IM service.Finally, the prospects in practical application are high-lighted.

Predictive Decision and Reliable Accessing for UAV Communication in Space-Air-Ground Integrated Networks

The cooperation of multiple Unmanned Aerial Vehicles(UAVs) has become a promising scenario in Space-Air-Ground Integrated Networks(SAGINs) recently due to their widespread applications,where wireless communication is a basic necessity and is normally categorized into control and nonpayload communication(CNPC) as well as payload communication. In this paper, we attempt to tackle two challenges of UAV communication respectively on establishing reliable CNPC links against the high mobility of UAVs as well as changeable communication conditions, and on offering dynamic resource optimization for Quality-of-Service(QoS) guaranteed payload communication with variable link connectivity. Firstly, we propose the concept of air controlling center(ACC), a virtual application equipped on the infrastructure in SAGINs, which can collect global information for estimating UAV trajectory and communication channels. We then introduce the knapsack problem for modelling resource optimization of UAV communication in order to provide optimal access points for both CNPC and payload communication. Meanwhile, using the air controlling information, predictive decision algorithm and handover strategy are introduced for the reliable connection with multiple access points. Simulation results demonstrate that our proposal ensures an approximate always-on reliable accessing of communication links and outperforms the existing methods against high mobility,sparse distribution, and physical obstacles.

ESMD-Flow: An Intelligent Flow Forwarding Scheme with Endogenous Security Based on Mimic Defense in Space-Air-Ground Integrated Network

The Space-Air-Ground Integrated Network(SAGIN) realizes the integration of space, air,and ground networks, obtaining the global communication coverage.Software-Defined Networking(SDN) architecture in SAGIN has become a promising solution to guarantee the Quality of Service(QoS).However, the current routing algorithms mainly focus on the QoS of the service, rarely considering the security requirement of flow. To realize the secure transmission of flows in SAGIN, we propose an intelligent flow forwarding scheme with endogenous security based on Mimic Defense(ESMD-Flow). In this scheme, SDN controller will evaluate the reliability of nodes and links, isolate malicious nodes based on the reliability evaluation value, and adapt multipath routing strategy to ensure that flows are always forwarded along the most reliable multiple paths. In addition, in order to meet the security requirement of flows, we introduce the programming data plane to design a multiprotocol forwarding strategy for realizing the multiprotocol dynamic forwarding of flows. ESMD-Flow can reduce the network attack surface and improve the secure transmission capability of flows by implementing multipath routing and multi-protocol hybrid forwarding mechanism. The extensive simulations demonstrate that ESMD-Flow can significantly improve the average path reliability for routing and increase the difficulty of network eavesdropping while improving the network throughput and reducing the average packet delay.

Intelligent Passive Detection of Aerial Target in Space-Air-Ground Integrated Networks

Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.

Civil Aircraft Assisted Space-Air-Ground Integrated Networks: Architecture Design and Coverage Analysis

In this paper, we propose a novel AIenabled space-air-ground integrated networks(SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called "civil aircraft assisted SAGIN(CAA-SAGIN)". The assistance of civil aircrafts can reduce the stress of satellite networks, improve the performance of SAGIN, decrease the construction cost and save space resources. Taking the Chinese mainland as an example, this paper has analyzed the distribution of civil aircrafts, and obtained the coverage characteristics of civil aircraft assisted networks(CAAN). Taking Starlink as the benchmark, this paper has calculated the service gap of CAAN, and designed the joint coverage constellation. The simulation results prove that the number of satellites in CAASAGIN can be greatly reduced with the assistance of civil aircrafts at the same data rate.

Three-Dimensional Cooperative Localization via Space-Air-Ground Integrated Networks

The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.

Software-Defined Space-Air-Ground Integrated Network Architecture with the Multi-Layer Satellite Backbone Network

Under the background of the rapid development of ground mobile communication,the advantages of high coverage,survivability,and flexibility of satellite communication provide air support to the construction of space information network.According to the requirements of the future space information communication,a software-defined Space-Air-Ground Integrated network architecture was proposed.It consisted of layered structure satellite backbone network,deep space communication network,the stratosphere communication network and the ground network.The Space-Air-Ground Integrated network was supported by the satellite backbone network.It provided data relay for the missions such as deep space exploration and controlled the deep-space spacecraft when needed.In addition,it safeguarded the anti-destructibility of stratospheric communication and assisted the stratosphere to supplement ground network communication.In this paper,algorithm requirements of the congestion control and routing of satellite backbone protocols for heterogeneous users’services were proposed.The algorithm requirements of distinguishing different service objects for the deep space communication and stratospheric communication network protocols were described.Considering the realistic demand for the dynamic coverage of the satellite backbone network and node cost,the multi-layer satellite backbone network architecture was constructed.On this basis,the proposed Software-defined Space-Air-Ground Integrated network architecture could be built as a large,scalable and efficient communication network that could be integrated into space,air,and ground.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

Static program analysis for IoT risk mitigation in space-air-ground integrated networks

The space-air-ground integrated networks(SAGINs)are pivotal for modern communication and surveillance,with a growing number of connected devices.The proliferation of Io T devices within these networks introduces new risks due to potential erroneous synergistic interactions that could compromise system integrity and security.This paper addresses the challenges in coordination,synchronization,and security within SAGINs by introducing a novel static program analysis(SPA)technique using zero-knowledge(ZK)proofs.This approach ensures the detection of risky interactions without compromising sensitive source code,thus safeguarding intellectual property and privacy.The proposed method overcomes the incompatibility between SPA and ZK systems by developing an imperative programming language for SAGINs and a specialized abstract domain for interaction threats.The system translates network control algorithms into arithmetic circuits suitable for ZK analysis,maintaining high accuracy in detecting risks.Evaluations of real-world scenarios demonstrate the system's efficacy in identifying risky interactions with minimal computational overhead.This research presents the first ZK-based SPA scheme for SAGINs,enhancing security and confidentiality in network analysis while adhering to privacy regulations.

Resource Allocation for Space-Air-Ground Integrated Networks:A Comprehensive Review

The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.

6G service-oriented space-air-ground integrated network: A survey

As an indispensable component of the emerging 6G networks,Space-Air-Ground Inte-grated Networks(SAGINs)are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks,aerial networks,and terrestrial networks.In 6G SAGINs,a wide variety of network services with the features of diverse requirements,complex mobility,and multi-dimensional resources will pose great challenges to service provisioning,which urges the develop-ment of service-oriented SAGINs.In this paper,we conduct a comprehensive review of 6G SAGINs from a new perspective of service-oriented network.First,we present the requirements of service-oriented networks,and then propose a service-oriented SAGINs management architec-ture.Two categories of critical technologies are presented and discussed,i.e.,heterogeneous resource orchestration technologies and the cloud-edge synergy technologies,which facilitate the interoperability of different network segments and cooperatively orchestrate heterogeneous resources across different domains,according to the service features and requirements.In addition,the potential future research directions are also presented and discussed.

Deep Learning-Based Spectrum Sensing in Space-Air-Ground Integrated Networks

To complement terrestrial connections,the space-air-ground integrated network(SAGIN)has been proposed to provide wide-area connections with improved quality of experience(QoE).Spectrum management is an important issue in SAGIN due to the explosive proliferation of wireless devices and services.While the progress on enabling dynamic spectrum access shows promise in advancing increased spectrum sharing,the issue of reliable spectrum sensing under low signal-to-noise ratio(SNR)remains one of the key challenges faced by the spectrum management.As artificial intelligence can provide wireless networks intelligence through learning and data mining,deep learning-based spectrum sensing is proposed in order to improve the spectrum sensing performance,where a deep neural network-based detection framework is built to extract features in a data-driven way based on the covariance matrix of the received signal.To eliminate the impact of noise uncertainty,a blind threshold setting scheme is proposed without using the system prior information.Numerical analyses on simulated and real-world signals show that the detection performance of the proposed scheme is improved under a low SNR regime.

A review of Space-Air-Ground integrated remote sensing techniques for atmospheric monitoring

Currently,the three-dimensional(3D)distribution and characteristics of air pollution cannot be understood based on the application of any single atmospheric monitoring technology.Long-term,high-precision and large-scale 3D atmospheric monitoring might become practical by combining heterogeneous modern technologies;for this purpose,the SpaceAir-Ground integrated system is a promising concept.In this system,optical remote sensing technologies employing fixed or mobile platforms are used as the main means for groundbased observations.Tethered balloons,unmanned aerial vehicles(UAV)and airborne platforms serve as the air-based observation segment.The final part,satellite remote sensing,corresponds to space-based observations.Aside from obtaining the 3D distribution of air pollution,research on emission estimation and pollution mechanisms has been extensively implemented based on the strengths of this system or some portion of it.Moreover,further research on the fusion of multi-source data,optimization of inversion algorithms,and coupling with atmospheric models is of great importance to the realization of this system.

Radio Resource Allocation for Bidirectional Offloading in Space-Air-Ground Integrated Vehicular Network

Aerial platforms and edge servers have been recognized as two promising building blocks to improve the quality of service(QoS)in space-air-ground integrated vehicular networks(SAGIN).Communication intensive tasks can be offloaded to aerial platforms via broadcasting,while computation intensive tasks can be offloaded to ground edge servers.However,the key issues including how to allocate radio resources and how to determine the task offloading strategy for the two types of tasks,are yet to be solved.In this paper,the joint optimization of radio resource allocation and bidirectional offloading configuration is investigated.To deal with the non-convex nature of the original problem,we decouple it into a two-step optimization problem.In the first step,we optimize the bidirectional offloading configuration in the case of the radio resource allocation known in advance,which is proved to be a convex optimization problem.In the second step,we optimize the radio resource allocation through a brute-force search method.We use queuing theories to analyze the average delay of the two tasks with respect to the broadcasting capacity and task arrival rate.The offloading strategies with closed-form expressions of communication intensive tasks are proposed.We then propose a heuristic algorithm which is shown to perform better than interior point algorithm in simulations.The numerical results also demonstrate that the aerial platforms and edge servers can significantly reduce the average delay of the tasks under different network conditions.

Space-air-ground integrated vehicular network for connected and automated vehicles:Challenges and solutions

Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles,in particular for new use cases like autonomous driving and vehicle platooning.In this paper,we propose a novel Space-Air-Ground integrated vehicular network(SAGiven)architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites,High-Altitude Platform stations(HAPs),low-altitude Unmanned Aerial Vehicles(UAVs),and terrestrial cellular communication systems.One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources.We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges.Specifically,the solutions will cover the following topics:(1)space-air-ground integrated network reconfiguration under dynamic space resources constraints;(2)multi-dimensional sensing and efficient integration of multi-dimensional context information;(3)real-time,reliable,and secure communications among vehicles and between vehicles and the SAGiven platform;and(4)a holistic integration and demonstration of the SAGiven.Finally,it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications.

Blockchain-envisioned unmanned aerial vehicle communications in space-air-ground integrated network: A review

Unmanned Aerial Vehicle(UAV)communications have recently entered a new period of interest,motivated by technological advances and the gradual emergence of the Space-Air-Ground Integrated Network(SAGIN).The current survey aims to capture the use of UAVs in the SAGIN while highlighting the most promising open research topics.The traditional UAV network architecture is not adequate to meet the challenges presented by the SAGIN,and an effective and secure space-air-ground integrated UAV network needs to be constructed.Given its well-distributed management and consensus mechanism,blockchain technology can make up for the deficiency of the traditional UAV network.In this work,we review the role of UAVs in the SAGIN.Then,three applications of the blockchain-envisioned UAV network are introduced through several classifications.Future challenges and the corresponding open research topics are also described.

铁路地质灾害勘察识别与监测预警

地质灾害对铁路建设、安全运营构成了极大威胁,“天空地”一体化综合勘察技术有效解决了地质灾害勘察识别难题。“天”基多源立体卫星遥感技术实现艰险复杂山区地质灾害的大范围精准判识,长时序InSAR与高精度GPS实现高陡岸坡的稳定性监测分析;“空”基真实感大场景、机载倾斜摄影和机载LiDAR扫描技术实现高植被覆盖区隐蔽性地质灾害判识;“地”基三维立体勘探技术获取岩土体结构、属性、参数;共同构建沟谷山地灾害链风险评价方案,为建设工程合理选址和工程设置提供依据。介绍地质灾害监测预警的方法、内容、技术和预警模型,对监测预警技术发展方向进行展望。

天空地一体化多目标跟踪算法研究综述

为实现全时全域“泛在连接”,构建天空地一体化网络已成为国家重大需求,而基于天空地一体化网络下跨域协同系统进行多目标跟踪是其中一个重要的发展方向,其在军民用领域都极具应用价值。本文详细阐述了天空地一体化网络背景下多目标跟踪方法研究进展。首先,介绍了天空地一体化跨域协同多目标跟踪的研究背景与意义。其次,从基于视觉的多目标跟踪、基于模型的多目标跟踪和基于多模态融合的多目标跟踪三个方面概述了当前的代表性研究方法:在基于视觉的多目标跟踪算法方面,介绍了单摄像头和多摄像头融合的多目标跟踪方法;对于基于模型的多目标跟踪,先介绍了单传感器多目标跟踪方法,以及在多种复杂场景下的改进,然后介绍了多传感器融合方法;在基于多模态信息融合的目标跟踪方面,在对多传感器时空配准方法和有代表性的多模态信息融合方法介绍的基础上,概括了基于多模态融合的多目标跟踪算法。最后探讨了当前存在的问题和未来发展方向:无论基于视觉的还是基于模型的多目标跟踪方法都有不少问题有待解决,特别是两种方法的结合值得深入研究;在面临复杂干扰时,基于多传感器信息融合的多目标跟踪由于能实现信息的互补,成为未来的主流发展方向;此外,跨域协同系统,由于能利用更多的资源和信息,其多目标跟踪问题研究极具价值,不过其中通信安全问题和多目标跟踪模型轻量化问题值得探讨。本文对从事目标跟踪及空天地一体化协同控制相关理论与技术研究的科研工作者具有重要参考价值。

卫星遥感水利应用进展与展望

监测感知数据是构建数字孪生水利体系的基础,卫星遥感是重要的监测感知手段。随着我国航天事业的蓬勃发展,依托国家科技重大专项、国家重点研发计划等科研项目开展技术攻关,以及国家防汛抗旱指挥系统工程等重大项目的推广应用,水利部构建了“采存算用全自主、遥感处理全链条、主要业务全覆盖、应用服务全贯通”的卫星遥感水利应用技术体系。卫星遥感技术在数字孪生水利数据底板建设、流域防洪、水资源管理与调配、河湖管理、水土保持、水利工程监测监管等业务工作中得到了广泛应用,发挥了显著成效。未来将从持续拓展卫星遥感影像资源、深度融合人工智能技术、强化“天空地”协同监测融合应用等方面,推动卫星遥感水利应用,赋能水利业务。