Artificial Intelligence Techniques for Next-Generation Mega Satellite
Networks
- URL: http://arxiv.org/abs/2207.00414v3
- Date: Sun, 17 Sep 2023 01:39:45 GMT
- Title: Artificial Intelligence Techniques for Next-Generation Mega Satellite
Networks
- Authors: Bassel Al Homssi, Kosta Dakic, Ke Wang, Tansu Alpcan, Ben Allen,
Russell Boyce, Sithamparanathan Kandeepan, Akram Al-Hourani, and Walid Saad
- Abstract summary: This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications.
It details the unique features of massive satellite networks, and the overarching challenges concomitant with their integration into the current communication infrastructure.
This entails applying AI for forecasting the highly dynamic radio channel, spectrum sensing and classification, signal detection and demodulation, inter-satellite and satellite access network optimization, and network security.
- Score: 37.87439415970645
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Space communications, particularly massive satellite networks, re-emerged as
an appealing candidate for next generation networks due to major advances in
space launching, electronics, processing power, and miniaturization. However,
massive satellite networks rely on numerous underlying and intertwined
processes that cannot be truly captured using conventionally used models, due
to their dynamic and unique features such as orbital speed, inter-satellite
links, short pass time, and satellite footprint, among others. Hence, new
approaches are needed to enable the network to proactively adjust to the
rapidly varying conditions associated within the link. Artificial intelligence
(AI) provides a pathway to capture these processes, analyze their behavior, and
model their effect on the network. This article introduces the application of
AI techniques for integrated terrestrial satellite networks, particularly
massive satellite network communications. It details the unique features of
massive satellite networks, and the overarching challenges concomitant with
their integration into the current communication infrastructure. Moreover, this
article provides insights into state-of-the-art AI techniques across various
layers of the communication link. This entails applying AI for forecasting the
highly dynamic radio channel, spectrum sensing and classification, signal
detection and demodulation, inter-satellite and satellite access network
optimization, and network security. Moreover, future paradigms and the mapping
of these mechanisms onto practical networks are outlined.
Related papers
- Hierarchical Learning and Computing over Space-Ground Integrated Networks [40.19542938629252]
We propose a hierarchical learning and computing framework to provide global aggregation services for locally trained models on ground IoT devices.
We formulate a network energy problem for model aggregation, which turns out to be a Directed Steiner Tree (DST) problem.
We propose a topologyaware energy-efficient routing (TAEER) algorithm to solve the DST problem by finding a minimum spanning arborescence on a substitute directed graph.
arXiv Detail & Related papers (2024-08-26T09:05:43Z) - Satellite Federated Edge Learning: Architecture Design and Convergence Analysis [47.057886812985984]
This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to mega-constellation networks.
By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL.
Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation.
arXiv Detail & Related papers (2024-04-02T11:59:58Z) - Cooperative Federated Learning over Ground-to-Satellite Integrated
Networks: Joint Local Computation and Data Offloading [33.44828515877944]
We propose a ground-to-satellite cooperative federated learning (FL) methodology to facilitate machine learning service management over remote regions.
Our methodology orchestrates satellite constellations to provide the following key functions during FL.
We show that our methodology can significantly speed up the convergence of FL compared with terrestrial-only and other satellite baseline approaches.
arXiv Detail & Related papers (2023-12-23T22:09:31Z) - Machine Learning-Based User Scheduling in Integrated
Satellite-HAPS-Ground Networks [82.58968700765783]
Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G)
This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
arXiv Detail & Related papers (2022-05-27T13:09:29Z) - Satellite Based Computing Networks with Federated Learning [30.090106801185886]
A new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI) has attracted substantial research interests.
Among various candidate technologies of 6G, low earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access.
To support massively interconnected devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we propose federated learning (FL) in LEO-based satellite communication networks.
arXiv Detail & Related papers (2021-11-20T13:24:23Z) - Deep Learning Aided Routing for Space-Air-Ground Integrated Networks
Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks.
With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links.
We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
arXiv Detail & Related papers (2021-10-28T14:12:10Z) - Intermittent Jamming against Telemetry and Telecommand of Satellite
Systems and A Learning-driven Detection Strategy [1.4620086904601468]
A security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems.
A learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed.
The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.
arXiv Detail & Related papers (2021-07-10T17:04:22Z) - Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms.
The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
arXiv Detail & Related papers (2021-01-25T13:01:16Z) - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning
for Non-Terrestrial Networks [51.05735925326235]
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation.
To maximize the end-to-end data rate, the satellite association and HAP location should be optimized.
We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
arXiv Detail & Related papers (2020-05-26T05:39:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.