What question did this study set out to answer?

The aim is to evaluate how well different neural network models can detect RF link anomalies under space propagation variations.

April 23, 2026Open Access

Analysis of RF Signal Anomaly Detection Performance Using Leaky Integrate-and-Fire Neuron-Based Spiking-NN in Space Propagation Environments

Key Points

The aim is to evaluate how well different neural network models can detect RF link anomalies under space propagation variations.
RF link anomalies were simulated by introducing noise and interference to ESA satellite telemetry data.
Leaky Integrate-and-Fire-based spiking neural network performance was compared with LSTM and 1D-CNN models.
Performance metrics included F1-score degradation under varying environmental conditions.
SNN exhibited the smallest F1-score degradation at 2.6% under increased environmental variations.
LSTM showed a degradation of 10.1%, while 1D-CNN had a larger degradation of 44.2%.
The findings emphasize the SNN's robustness in maintaining operational availability for satellite communications.

Abstract

This study evaluates the RF link anomaly detection under space propagation variations. Noise, signal loss, and interference were progressively added to the European Space Agency (ESA) satellite telemetry data, and the performance of a Leaky Integrate-and-Fire (LIF)-based spiking neural network (SNN) was compared with those of the long short-term memory (LSTM) and 1D-convolutional neural network (CNN) models. While the performance differences were minimal under normal conditions, the SNN showed the smallest F1-score degradation (2.6 %) as environmental variations increased, outperforming LSTM (10.1 %) and 1D-CNN (44.2 %). These results highlight the importance of robustness, and indicate that LIF-based SNN is effective for bit error rate (BER) management and operational availability in satellite communication systems.

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Cite This Study

Lee et al. (Sun,) studied this question.

synapsesocial.com/papers/69e9b6aa85696592c86eafcd https://doi.org/https://doi.org/10.5515/kjkiees.2026.37.3.251

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