Space-air-ground Integrated Network Research Articles

Reinforcement Learning-Based Resource Allocation and Energy Efficiency Optimization for a Space–Air–Ground-Integrated Network

Reinforcement Learning-Based Resource Allocation and Energy Efficiency Optimization for a Space–Air–Ground-Integrated Network

Blockchain-based secure communication of internet of things in space–air–ground integrated network

Under the Internet of Things (IoT) era, the cooperation of multiple service providers has facilitated the development of large-scale distributed IoT with a strong demand for reliable and real-time communications. The Space–Air–Ground Integration Network (SAGIN) can be seen as an extension of the traditional terrestrial network with open and ubiquitous connectivity that can be used to support the future IoT in the 6G era. However, the deployment of SAGIN is fraught with numerous challenges, particularly in terms of security. As an emerging technology, blockchain has received academic attention in mitigating the trust problem of SAGIN communication systems. In this paper, we introduce a decentralized data transmission framework that combines the characteristics of SAGIN and blockchain. This framework leverages the increasing computational and communication capabilities of low Earth orbit satellites. Subsequently, we provide an overview of the three-layer structure of this framework and detail the four working steps of the transmission mechanism. Simulation results show that our proposed scheme has excellent performance in terms of latency and throughput while ensuring the security of data transmission.

Secrecy Performance of a Non-Orthogonal Multiple Access-Based Space–Air–Ground Integrated Network System with Stochastic Geometry Distribution of Terrestrial Terminals and Fog Absorption in Optical Link

Recently, non-orthogonal multiple access (NOMA)-based space–air–ground integrated networks (SAGINs) have gained increasing attention due to their robust communication, broader coverage, and resource-saving advantages. However, it is imperative to consider physical layer security as a crucial performance metric in NOMA-based SAGINs. This study addresses this concern by constructing a NOMA-based free space optical (FSO)/radio frequency (RF) dual-hop SAGIN system with eavesdroppers on both links. The two new fading channel models were proposed, considering the FSO link’s fog absorption and the RF link’s stochastic distribution based on Málaga and shadowed Rician distributions. The closed-form expressions for the secrecy outage probability are derived for the SAGIN system. Monte Carlo simulations were conducted to validate the theoretical findings. The results revealed the influence of fog absorption and the stochastic geometry distribution on the SAGIN system.

Edge computing offloading strategy for space-air-ground integrated network based on game theory

The limited coverage of terrestrial networks makes it difficult to provide low-latency and high-reliable computing services for users and Internet of Thing (IoT) devices in remote areas such as mountainous regions and oceans. Space-Air-Ground Integrated Network (SAGIN) combined with Mobile Edge Computing (MEC) can provide seamless three-dimensional services for users by deploying edge servers on satellites, Unmanned Aerial Vehicles (UAVs) and ground infrastructures. However, due to the limited heterogeneous computing resources in satellites-UAV clusters network and the energy resources of IoT devices, it brings a significant challenge in determining how to offload the computing tasks generated by ground user devices to satellite edge nodes, UAV edge nodes or locally for processing. In this paper, we first propose a satellites-UAV clusters-ground three-layer edge computing network architecture consisting of a global controller, inter-domain controllers and MEC servers. We then model the task offloading problem as a Binary Integer Linear Programming (BILP) aiming at minimizing the offloading cost composed of delay and energy consumption and prove it is NP-hard. Next, the original offloading problem is transformed to a noncooperative strategic game and the existence of Nash equilibrium is proven using potential games. Finally, we propose a Nash Equilibrium Iteration Offloading algorithm based on Game theory (NEIO-G) to find the optimal offloading strategy. Compared with other baseline algorithms, simulation results demonstrate that the NEIO-G can significantly reduce the system offloading overhead in terms of delay and energy consumption.

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.

Terahertz-Band Near-Space Communications: From a Physical-Layer Perspective

Facilitated by the rapid technological development of the near-space platform stations (NSPS), near-space communication (NS-COM) is envisioned to play a pivotal role in the space-air-ground integrated network (SAGIN) for the sixth generation (6G) communications and beyond. In NS-COM, ultrabroadband wireless connectivities between NSPSs and various airborne/spaceborne platforms are required for a plethora of bandwidth-consuming applications, such as NSPS-based ad hoc networking, the in-flight Internet and the relaying technology. However, such requirement seem to contradict the scarcity of spectrum resources at conventional microwave frequencies, which motivates the exploitation of terahertz (THz) band ranging from 0.1 to 10 THz. Due to huge available bandwidth, THz signals are capable of supporting ultra-high-rate data transmission for NS-COM over 100 Gb/s, which are naturally suitable for the near-space environment with marginal path loss. Against this background, this article provides an extensive investigation on THz-band NS-COM (THz-NS-COM) from a physical-layer perspective. Firstly, we summarize the potential applications of THz communications in the near-space environment, where the corresponding technical barriers are analyzed. Secondly, the channel characteristics of THz-NS-COM and the corresponding modeling strategies are discussed. Thirdly, three essential research directions are investigated to surpass the technical challenges of THz-NS-COM, i.e., robust beamforming for ultramassive antenna array, signal processing algorithms against hybrid distortions, and integrated sensing and communications. Several open problems are also provided to unleash the full potential of THz-NS-COM.

A service function chain mapping scheme based on functional aggregation in space-air-ground integrated networks

Being a novel network architecture, the Space-air-ground integrated network (SAGIN) offers advantages such as extensive network coverage and seamless ubiquitous access. However, it also encounters the challenge of balancing a growing user demand with limited network service resources. In order to further improve the utilization rate of network resources, a group of virtual network functions can be connected according to certain business logic to form a dynamic reconfigurable service function chain. This can provide diversified and high-quality network services for users. So by aggregating the same type of network function, we can reduce the cost of service chain mapping. Based on the above ideas, this paper proposes an Aggregation of Service Function Chain mapping algorithm (A-SFC). The aggregated service function chain topology is mapped according to an improved isomorphic graph search mapping algorithm, resulting in a deployment solution that minimizes the mapping cost by aggregating static SFC requests by end-system division. Experimental results show that the algorithm is able to find an effective mapping scheme under resource constraints. In addition to reducing computation, the algorithm effectively minimizes the consumption of computational resources while delivering excellent performance in terms of service delay and mapping cost.

Multiagent Reinforcement Learning-Based Orbital Edge Offloading in SAGIN Supporting Internet of Remote Things

We investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for Internet of remote Things (IoRT). In the considered scenario, the unmanned aerial vehicles (UAVs) collect computing tasks from IoRT devices and make offloading decisions, in which the tasks can be computed at the UAVs or offloaded to the low earth orbital (LEO) satellite. The optimization objective is to design offloading strategies for each UAV that maximum the number of tasks satisfies delay constraint and reduces the energy consumption of UAVs. We formulate this problem as a nonlinear integer optimization problem, and remodel it as a stochastic game to solve it. To copy with the dynamic and complexity environment, we propose a learning-based orbital edge offloading (LOEF) approach which can coordinate all UAVs to learn the optimal offloading strategies. This method is based on the Actor-Critic framework of multi-agent reinforcement learning, the Actor observes the environment and output the current offloading decision, the Critic evaluates the action of the Actor and coordinate the actions of all UAV to increase the reward of system. The simulation results show that the proposed offloading strategy converges fast, increases the number of satisfying the delay constraints tasks and the energy utilization of UAVs compared with the baseline strategies.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.

Secure Federated Learning in Quantum Autonomous Vehicular Networks

With rapid developments of artificial intelligence (AI) and wireless communication technologies, autonomous vehicular networks (AVNs) have emerged to provide convenient, comfortable, and safe driving services. Especially, due to the provable and unconditional security, quantum communication technology is increasingly being applied to promote the security of data delivery in AVNs, which forms the promising quantum AVNs (QAVNs). Federated learning (FL) is essential in QAVNs to implement intelligent and autonomous driving, where extensive distributed data is utilized to cooperatively train AI models, preserve user privacy, and reduce resource consumption. However, the FL unavoidably faces several possible privacy and security problems as a result of the intricate interconnections between several autonomous vehicles or infrastructures. In this article, we investigate the security and privacy of FL in QAVNs. Specifically, we first establish the system architecture, including quantum key distribution protocol, space-air-ground integrated network (SAGIN)-enabled QAVNs model, and FL framework in QAVNs. Furthermore, we describe the privacy and security threats to FL in QAVNs, i.e., inference attack, eavesdropping attack, and sybil attack. Following that, we give a case study for designing a secure FL scheme using the local differential privacy mechanism and the homomorphic encryption mechanism. Finally, we suggest several potential future research directions in QAVNs.

An intelligent encryption decision method for autonomous domain of multilayer satellite network

Multi-layer satellite network (MLSN) is an important part of the Space-Air-Ground Integrated Network (SAGIN), it has obvious advantages in coverage density, business processing, service efficiency and other comprehensive capabilities compared with ordinary single-layer satellite networks. However, due to the fact that many space nodes, highly dynamic topology changes and wireless links scattered in free space, MLSNs are facing the dual challenges of inefficient organization and lack of security. In this case, this paper considers providing secure encryption decision support for the return of data obtained by low earth orbit (LEO) observation satellites relying on the local autonomous region of multi-layer satellite space. By quantifying the security strength of encryption algorithms, the work to be optimized is described as the problem of maximizing the security strength under the constraint of return delay of the data. This paper thus proposes a solution based on deep reinforcement learning (DRL), which relies on reasonably setting the key supporting elements such as state, action and reward closely related to the problem to achieve fast and reliable decision output, and the simulation results show that the proposed method has better performance than many comparison methods.

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the network. To address these issues, we first propose a SAGIN architecture with energy-harvesting devices, where the base station (BS) is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power an unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighboring BSs is designed to fully utilize RE to reduce carbon emissions. Secondly, on the basis of task offloading decisions, the UAV trajectory, and the RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emissions.

Secure and Efficient UAV Tracking in Space-Air-Ground Integrated Network

With the development of 5 G and other communication techniques, the space-air-ground integrated network (SAGIN) is regarded as a promising solution to provide wide-range, cost-effective, and real-time wireless access. Unmanned Aerial Vehicle (UAV) Tracking plays a crucial role in guaranteeing the performance of SAGIN. However, UAV tracking still faces a series of challenges in the real-world deployment, especially in the case of the presence of malicious attackers, who may launch a series of attacks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , message spoofing attack, routing misbehavior attack) to disrupt the systems. In this study, to enhance the security and efficiency of SAGIN when conducting object tracking, we propose a novel and secure object tracking system named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecTracker</small> to overcome the emerging security problems in the SAGIN. Focusing on the unmanned aerial vehicle (UAV) tracking, we design the Local Voting based Detection Module to defend the message spoofing attack and implement the Routing Evidence based Detection Module to defend the routing misbehavior attack. Besides, efficiency enhancement mechanisms ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , probabilistic detection algorithm and game theory based algorithm) are proposed in this paper to improve the efficiency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecTracker</small> . Considering the above-mentioned attacks, the overall tracking accuracy is improved from 88.16% in existing schemes to 96.64% in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecTracker</small> , which demonstrates the effectiveness of the proposed system.

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.

Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network

The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services. However, training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. To tackle these challenges, we first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL). Specifically, the IoRT devices in the ground layer leverage their private data to perform model training locally, while the air nodes in the air layer and the ring-structured low earth orbit (LEO) satellite constellation in the space layer are in charge of model aggregation (synchronization) at different scales. To further enhance communication efficiency and inference performance of OBL, an efficient Communication and Non-IID-aware Air node-Satellite Assignment (CNASA) algorithm is designed by taking the data class distribution of the air nodes as well as their geographic locations into account. Furthermore, we extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks. We analyze the convergence of our OBL framework and conclude that the CNASA algorithm contributes to the fast convergence of the global model. Extensive experiments based on realistic datasets corroborate the superior performance of our algorithm over the benchmark policies.

Risk-Aware Distributionally Robust Optimization for Mobile Edge Computation Task Offloading in the Space–Air–Ground Integrated Network

As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and storage resources in mobile devices greatly impacts the quality of experiences for intelligent applications. Hence, we plan to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MECs). To facilitate efficient processing, we need to solve the optimal task offloading decisions. In contrast to existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capabilities for edge computing nodes, the uncertainty of transmission latency caused by heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we first describe the task offloading decision problem in environments characterized by these new challenges. However, we cannot use standard robust optimization and stochastic optimization methods to obtain optimal results under uncertain network environments. In this paper, we propose the ‘condition value at risk-aware distributionally robust optimization’ algorithm for task offloading, denoted as RADROO, to solve the task offloading decision problem. RADROO combines the distributionally robust optimization and the condition value at risk model to achieve optimal results. We evaluated our approach in simulated SAGIN environments, considering confidence intervals, the number of mobile task offloading instances, and various parameters. We compare our proposed RADROO algorithm with state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the Brute algorithm. The experimental results show that RADROO can achieve a sub-optimal mobile task offloading decision. Overall, RADROO is more robust than others to the new challenges mentioned above in SAGIN.

Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space-air-ground integrated network (SAGIN) provides a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multi-dimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This paper proposes a service provision method based on Service Function Chaining (SFC) to solve this problem. Network Function Virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federation learning (FL) based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue-cost ratio, respectively.

Channel Modeling and Estimation for Reconfigurable-Intelligent-Surface-Based 6G SAGIN IoT

The design of Internet of Thing (IoT) system over space-air-ground integrated network (SAGIN) is still in its infancy. It is critical to get 6G technology involved, in order to address the current problems in the combined SAGIN and IoT ecosystem. This work utilizes reconfigurable intelligent surface (RIS) to enhance the existing SAGIN infrastructure. We highlight channel modeling and estimate as a major component of RIS beam control and present a sparse Bayesian superresolution estimation approach to achieve a good balance of accuracy and computing complexity. Our numerical results justify that, with our proposed models and algorithms, RIS will become a cost-effective and spectral-efficient solution for SAGINbased 6G networks.

New delay Doppler communication paradigm in 6G era: A survey of orthogonal time frequency space (OTFS)

In the 6G era, Space-Air-Ground Integrated Network (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication systems, experiences performance degradation due to significant Doppler shifts. To overcome this obstacle, a novel two-dimensional (2D) modulation approach, namely orthogonal time frequency space (OTFS), has emerged as a key enabler for future high-mobility use cases. Distinctively, OTFS modulates information within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF) domain utilized by OFDM. This offers advantages such as Doppler and delay resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR), and a reduced-complexity implementation. Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications (ISAC). In this article, we present an in-depth review of OTFS technology in the context of the 6G era, encompassing fundamentals, recent advancements, and future directions. Our objective is to provide a helpful resource for researchers engaged in the field of OTFS.

DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks

Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.