Autors: Syed, S.N., Lazaridis, P.I., Khan, F.A., Ahmed, Q.Z., Hafeez, M., Ivanov, A. S., Poulkov, V. K., Zaharis, Z.D.
Title: Deep Neural Networks for Spectrum Sensing: A Review
Keywords: 6G; Autoencoders; cognitive radio

Abstract: As we advance towards 6G communication systems, the number of network devices continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS), Cognitive Radio (CR) exploits the frequency spectrum dynamically by detecting and transmitting in underutilized bands. The performance of 6G networks can be enhanced by utilizing Deep Neural Networks (DNNs) to perform SS. This paper provides a detailed survey of several Deep Learning (DL) algorithms used for SS by classifying them as Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, combined CNN-LSTM architectures and Autoencoders (AEs). The works are discussed in terms of the input provided to the DL algorithm, data acquisition technique used, data pre-processing technique used, architecture of each algorithm, evaluation metrics used, results obtained, and comparison with standard SS detectors.

References

    Issue

    IEEE Access, vol. 11, pp. 89591-89615, 2023, United States, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/ACCESS.2023.3305388

    Copyright IEEE

    Цитирания (Citation/s):
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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science