Neuromorphic Models for Energy-Efficient Onboard Interference Detection in Satellite Systems

convolutional neural networks; energy-efficient; Interference detection; neuromorphic computing; satellite communication; spiking neural networks; Convolutional neural network; Detection and identifications; Energy efficient; Interference identifications; Neural-networks; Neuromorphic computing; Radio frequency interference; Satellite communications; Spiking neural network; Aerospace Engineering; Electrical and Electronic Engineering

Abstract :

[en] The surge in the number of non-geostationary satellites has intensified interference issues. Interference detection in satellite communication with onboard processing is critical, as it relies heavily on accurately identifying interference events to ensure seamless and reliable data transmission. This paper delves into the critical problem of onboard interference detection and identification within satellite communication systems. A scenario comprising a satellite that receives radio frequency (RF) interference from a ground terminal not belonging to its network is considered. Conventional techniques, such as energy detection (ED), have shown difficulties in detecting weak interferences. In this context, artificial intelligence (AI)-based techniques are a promising solution to enhance performance when addressing RF interference. In particular, the case of brain-inspired spiking neural networks (SNNs) for energy-efficient implementations is studied. The primary contributions of this work encompass the creation of a labeled dataset for interference detection in noisy channel environments, the implementation of both SNN and convolutional neural network (CNN) models for interference detection and classification, and a thorough evaluation of their performance for onboard interference detection and identification in actual commercially available AI-acceleration chipset of Xilinx and Intel's research neuromorphic processor, Loihi 2. The results confirm that SNNs implemented on neuromorphic chips can offer orders of magnitude improvements in energy efficiency compared to CNNs implemented on specialist hardware while maintaining accuracy.

Disciplines :

Computer science

Author, co-author :

Eappen, Geoffrey ; University of Luxembourg, Interdisciplinary Centre for Security Reliability and Trust, Luxembourg City, Luxembourg ; OQtechnology, Luxembourg

DAOUD, Saed Shaheer Awad ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

Skatchkovsky, Nicolas; Francis Crick Institute, United Kingdom

Ortiz, Flor ; University of Luxembourg, Interdisciplinary Centre for Security Reliability and Trust, Luxembourg City, Luxembourg

LAGUNAS, Eva ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

Martins, Wallace A. ; Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France

CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

External co-authors :

yes

Language :

English

Title :

Neuromorphic Models for Energy-Efficient Onboard Interference Detection in Satellite Systems

Publication date :

10 September 2025

Journal title :

IEEE Transactions on Aerospace and Electronic Systems

ISSN :

0018-9251

eISSN :

1557-9603

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Pages :

1-24

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/7/7778228/11155214.pdf?arnumber=11155214

Funding text :

Symeon Chatzinotas, Fellow, IEEE Interdisciplinary Centre for Security Reliability and Trust, University of Luxembourg, 1855 Luxembourg City, Luxembourg This work has been supported by the European Space Agency (ESA) funded under Contract No. 4000137378/22/UK/ND - The Application of Neuromorphic Processors to Satcom Applications. Please note that the views of the authors of this paper do not necessarily reflect the views of ESA. Furthermore, this work was partially supported by the Luxembourg National Research Fund (FNR) under the project SmartSpace (C21/IS/16193290), the Marie Speyer Excellence Grant 2024 (BrainSat) and the FNR BrainSatCom project (BRIDGES/2024/IS/19003118). Corresponding author: Geoffrey Eap-pen (email: geoffrey.eappen@oqtec.com). *These authors contributed equally to this work.

Available on ORBilu :

since 08 December 2025

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