  With the rapid growth of IoT networks, ubiquitous coverage is becoming
increasingly necessary. Low Earth Orbit (LEO) satellite constellations for IoT
have been proposed to provide coverage to regions where terrestrial systems
cannot. However, LEO constellations for uplink communications are severely
limited by the high density of user devices, which causes a high level of
co-channel interference. This research presents a novel framework that utilizes
spiking neural networks (SNNs) to detect IoT signals in the presence of uplink
interference. The key advantage of SNNs is the extremely low power consumption
relative to traditional deep learning (DL) networks. The performance of the
spiking-based neural network detectors is compared against state-of-the-art DL
networks and the conventional matched filter detector. Results indicate that
both DL and SNN-based receivers surpass the matched filter detector in
interference-heavy scenarios, owing to their capacity to effectively
distinguish target signals amidst co-channel interference. Moreover, our work
highlights the ultra-low power consumption of SNNs compared to other DL methods
for signal detection. The strong detection performance and low power
consumption of SNNs make them particularly suitable for onboard signal
detection in IoT LEO satellites, especially in high interference conditions.
