Abstract
Spectrum sensing techniques have many challenges, and as one as the most challenges are the noise and interference rejection. Since the occurrence of noise power uncertainty causes the degradation of the performance of the spectrum detector. One of the most techniques of spectrum sensing is the energy level detection, it could be used with deep learning network to distinguish between presence of signal and noise to this end, and we will introduce a comparison between AlexNet, SqueezeNet, ResNet101, and LSTM neural networks. To test them in different situations.
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Ouamna, H., Madini, Z., Zouine, Y. (2023). Deep Learning Applied for Spectrum Sensing. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 465. Springer, Singapore. https://doi.org/10.1007/978-981-19-2397-5_70
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DOI: https://doi.org/10.1007/978-981-19-2397-5_70
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