129 research outputs found
Security-aware Resource Allocation for Space-Air-Ground Integrated Network using Deep Reinforcement Learning
A Space-Air-Ground Integrated Network (SAGIN) has been proposed to extend communication network service coverage to consumer-oriented and industrial sectors where communication network coverage is either limited or unavailable. To effectively use the space, air, and ground hardware resources, Network Function Virtualization (NFV) is introduced into SAGIN. NFV enables the deployment and management of services that are represented as Virtual Networks (VN) composed of Virtual Network Functions (VNF) onto the SAGIN hardware through hardware virtualization. This enables SAGIN to support services with distinct demands from both consumer-oriented and industrial sectors.
However, by introducing NFV into SAGIN, new security vulnerabilities arise. For instance, if a malicious entity gains access to the virtualized hardware, all services utilizing the hardware are exposed to attack.
When deploying a VN onto the SAGIN hardware, also known as the Substrate Network (SN), it must be decided which SN Node (SNN) should host each VN Node (VNN) and which SN Links (SNL) should host each VN Link (VNL), also known as the Virtual Network Embedding (VNE) problem. This thesis proposes a solution to VNE in SAGIN using Deep Reinforcement Learning (DRL) while accounting for the security concerns related to NFV. To our knowledge, this has yet to be explored by other works.
We compare our solution with the well-known Global Resource Capacity (GRC) solution strategy using the acceptance rate, revenue, cost, and revenue-to-cost metrics. Our DRL-based solution strategy shows competitive performance in all metrics
Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms
As an attractive enabling technology for next-generation wireless
communications, network slicing supports diverse customized services in the
global space-air-ground integrated network (SAGIN) with diverse resource
constraints. In this paper, we dynamically consider three typical classes of
radio access network (RAN) slices, namely high-throughput slices, low-delay
slices and wide-coverage slices, under the same underlying physical SAGIN. The
throughput, the service delay and the coverage area of these three classes of
RAN slices are jointly optimized in a non-scalar form by considering the
distinct channel features and service advantages of the terrestrial, aerial and
satellite components of SAGINs. A joint central and distributed multi-agent
deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving
the above problem to obtain the Pareto optimal solutions. The algorithm first
determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the
inter-slice sub-channel and power sharing by relying on a centralized unit.
Then it optimizes the intra-slice sub-channel and power allocation, and the
virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite
deployment in support of three classes of slices by three separate distributed
units. Simulation results verify that the proposed method approaches the
Pareto-optimal exploitation of multiple RAN slices, and outperforms the
benchmarkers.Comment: 19 pages, 14 figures, journa
Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks
The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous
devices including low earth orbit (LEO) satellites, unmanned aerial vehicles
(UAVs), and ground users (GUs), holds significant promise for advancing smart
city applications. However, resource management of the SAGIN is a challenge
requiring urgent study in that inappropriate resource management will cause
poor data transmission, and hence affect the services in smart cities. In this
paper, we develop a comprehensive SAGIN system that encompasses five distinct
communication links and propose an efficient cooperative multi-type multi-agent
deep reinforcement learning (CMT-MARL) method to address the resource
management issue. The experimental results highlight the efficacy of the
proposed CMT-MARL, as evidenced by key performance indicators such as the
overall transmission rate and transmission success rate. These results
underscore the potential value and feasibility of future implementation of the
SAGIN
Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends
The sixth-generation (6G) network is expected to achieve global coverage
based on the space-air-ground integrated network, and the latest satellite
network will play an important role in it. The introduction of inter-satellite
links (ISLs) can significantly improve the throughput of the satellite network,
and recently gets lots of attention from both academia and industry. In this
paper, we illustrate the advantages of using the laser for ISLs due to its
longer communication distance, higher data speed, and stronger security.
Specifically, space-borne laser terminals with the acquisition, pointing and
tracking mechanism which realize long-distance communication are illustrated,
advanced modulation and multiplexing modes that make high communication rates
possible are introduced, and the security of ISLs ensured by the
characteristics of both laser and the optical channel is also analyzed.
Moreover, some open issues such as advanced optical beam steering, routing and
scheduling algorithm, and integrated sensing and communication are discussed to
direct future research
A Universal Attenuation Model of Terahertz Wave in Space-Air-Ground Channel Medium
Providing continuous bandwidth over several tens of GHz, the Terahertz (THz)
band (0.1-10 THz) supports space-air-ground integrated network (SAGIN) in 6G
and beyond wireless networks. However, it is still mystery how THz waves
interact with the channel medium in SAGIN. In this paper, a universal
space-air-ground attenuation model is proposed for THz waves, which
incorporates the attenuation effects induced by particles including condensed
particles, molecules, and free electrons. The proposed model is developed from
the insight into the attenuation effects, namely, the physical picture that
attenuation is the result of collision between photons that are the essence of
THz waves and particles in the environment. Based on the attenuation model, the
propagation loss of THz waves in the atmosphere and the outer space are
numerically assessed. The results indicate that the attenuation effects except
free space loss are all negligible at the altitude higher than 50 km while they
need to be considered in the atmosphere lower than 50 km. Furthermore, the
capacities of THz SAGIN are evaluated in space-ground, space-sea, ground-sea,
and sea-sea scenarios, respectively
Generative AI for Space-Air-Ground Integrated Networks (SAGIN)
Recently, generative AI technologies have emerged as a significant
advancement in artificial intelligence field, renowned for their language and
image generation capabilities. Meantime, space-air-ground integrated network
(SAGIN) is an integral part of future B5G/6G for achieving ubiquitous
connectivity. Inspired by this, this article explores an integration of
generative AI in SAGIN, focusing on potential applications and case study. We
first provide a comprehensive review of SAGIN and generative AI models,
highlighting their capabilities and opportunities of their integration.
Benefiting from generative AI's ability to generate useful data and facilitate
advanced decision-making processes, it can be applied to various scenarios of
SAGIN. Accordingly, we present a concise survey on their integration, including
channel modeling and channel state information (CSI) estimation, joint
air-space-ground resource allocation, intelligent network deployment, semantic
communications, image extraction and processing, security and privacy
enhancement. Next, we propose a framework that utilizes a Generative Diffusion
Model (GDM) to construct channel information map to enhance quality of service
for SAGIN. Simulation results demonstrate the effectiveness of the proposed
framework. Finally, we discuss potential research directions for generative
AI-enabled SAGIN.Comment: 9page, 5 figure
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms
Recent technological advancements in space, air and ground components have
made possible a new network paradigm called "space-air-ground integrated
network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs.
However, due to UAVs' high dynamics and complexity, the real-world deployment
of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the
space and terrestrial components, UAVs are expected to meet performance
requirements with high flexibility and dynamics using limited resources.
Therefore, employing UAVs in various usage scenarios requires well-designed
planning in algorithmic approaches. In this paper, we provide a comprehensive
review of recent learning-based algorithmic approaches. We consider possible
reward functions and discuss the state-of-the-art algorithms for optimizing the
reward functions, including Q-learning, deep Q-learning, multi-armed bandit
(MAB), particle swarm optimization (PSO) and satisfaction-based learning
algorithms. Unlike other survey papers, we focus on the methodological
perspective of the optimization problem, which can be applicable to various
UAV-assisted missions on a SAGIN using these algorithms. We simulate users and
environments according to real-world scenarios and compare the learning-based
and PSO-based methods in terms of throughput, load, fairness, computation time,
etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional
(3D) variations of these algorithms to reflect different deployment cases. Our
simulation suggests that the D satisfaction-based learning algorithm
outperforms the other approaches for various metrics in most cases. We discuss
some open challenges at the end and our findings aim to provide design
guidelines for algorithm selections while optimizing the deployment of
UAV-assisted SAGINs.Comment: Submitted to the IEEE Internet of Things Journal in June 202
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