Spiking Neural Networks for Detecting Satellite Internet of Things Signals

With the rapid growth of Internet of Things (IoT) networks, ubiquitous coverage is becoming increasingly necessary. Low earth orbit (LEO) satellite constellations for the 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 cochannel interference. This research presents a 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, due to their capacity to effectively distinguish target signals amid cochannel interference. Moreover, our work highlights the ultralow 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.