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Spiking-UNet: Spiking Neural Networks for Spectrum Occupancy Monitoring
  • Kosta Dakic ,
  • Bassel Al Homssi ,
  • Akram Al-Hourani
Kosta Dakic
RMIT University

Corresponding Author:[email protected]

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Bassel Al Homssi
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Akram Al-Hourani
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Abstract

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 a groundbreaking 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 leverages the leaky integrate-and-fire neuron model and provides superior energy efficiency, real-time inference capability, and higher detection performance. Through extensive simulations, the proposed SNN framework exhibited performance metrics that significantly surpass energy detection methods and closely align with conventional convolutional neural network techniques. Future explorations will delve into enhancing the framework using machine learning techniques for advanced feature extraction and multi-class segmentation.