Spiking-UNet: Spiking Neural Networks for Spectrum Occupancy Monitoring

With the exponential growth of the Internet of Things (IoT) landscape and the resulting spectrum congestion, innovative techniques for spectrum monitoring are crucial. This paper presents an approach to spectrum monitoring harnessing the power of spiking neural networks (SNNs) with a focus on image segmentation using the UNet architecture. Traditional methods, including energy detection, have been widely used but are not without challenges, especially in environments with varying signal-to-noise ratios. In contrast, the presented SNN approach in this paper demonstrates through simulations performance metrics that significantly surpass energy detection methods and closely align with conventional convolutional neural network techniques while also exhibiting favorable energy efficiency. Future explorations will delve into enhancing the framework using machine learning techniques for advanced feature extraction and multi-class segmentation.