Artificial intelligence for satellite communication: A review

Abstract

Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects has 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. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks

“…Although AI-based optimization of communication systems has been reported in previous studies [7], [8], we discuss the role of AI in empowering networks in rural areas. This discussion will emphasize the role of AI in future communication systems, especially in challenging contexts such as rural areas.…”

“…Fronthaul and backhaul solutions for Internet of Things (IoT) in rural areas are discussed in [5]. The use of AI for the optimization of communication systems has been reviewed in various papers, including the use of AI in satellite communications [7], and unmanned aerial vehicules (UAV) communications [8].…”

“…In fact, AI can be useful to optimize each segment. For example, in satellite communication (the space segment) [7], AI has demonstrated promise in behavior modeling, channel modeling, network traffic forecasting, remote sensing, interference managing, ionospheric scintillation detection, telemetry mining, antijamming, beam-hopping, and energy management.…”

“…Further, AI can be used in space-air-ground integration [7]. However, wireless network optimization using classical techniques may not be practical for SAGINs, as the networks are highly complex and dynamic.…”

Performance Analysis of NOMA Integrated Hybrid Satellite Terrestrial Communication System using ML

“…All the three branches of AI are very useful in innumerable applications. In [40], a detailed review of AI for satellite communication has been presented and discussed various features, future scopes and state of art algorithms of AI in existing as well as emerging elds. It is shown in recent studies that with the help of ML algorithms, the successful results can be achieved in diverse applications such as satellite communication, wireless communication, channel modelling, network tra c forecasting, anti-jamming, wireless network design [41] and satellite-terrestrial networking [42].…”