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.

翻译：随着物联网网络的快速发展，全域覆盖需求日益迫切。低轨卫星星座作为物联网的延伸方案，可为地面系统无法覆盖的区域提供服务。然而，上行链路的低轨卫星通信系统受限于高密度用户设备引发的严重同信道干扰。本研究提出一种新颖框架，利用脉冲神经网络在存在上行干扰的环境中检测物联网信号。脉冲神经网络的核心优势在于其功耗远低于传统深度学习网络。我们将基于脉冲神经网络的检测器性能与最先进的深度学习网络及传统匹配滤波检测器进行比较。结果表明：在强干扰场景下，深度学习和脉冲神经网络接收器均能凭借其有效区分同信道干扰中目标信号的能力，显著优于匹配滤波检测器。此外，本研究凸显了脉冲神经网络在信号检测方面相较于其他深度学习方法的超低功耗特性。脉冲神经网络兼具优异的检测性能与极低功耗，特别适用于高干扰环境下低轨卫星物联网星载信号检测。