Affiliation:
1. Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg
Abstract
Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.
Funder
European Space Agency
Luxembourg National Research Fund
Reference50 articles.
1. Machine Learning for Satellite Communications Operations;Vazquez;IEEE Commun. Mag.,2021
2. Ortiz-Gomez, F.G., Lei, L., Lagunas, E., Martinez, R., Tarchi, D., Querol, J., Salas-Natera, M.A., and Chatzinotas, S. (2022). Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems. Electronics, 11.
3. Satellite Communications in the New Space Era: A Survey and Future Challenges;Kodheli;IEEE Commun. Surv. Tutorials,2021
4. Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions;Lee;IEEE Access,2019
5. Machine Learning Paradigms for Next-Generation Wireless Networks;Jiang;IEEE Wirel. Commun.,2017
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献