Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs
Department
Electrical and Computer Engineering
Document Type
Article
Publication Date
1-1-2023
Abstract
Spectrum sensing is a critical component of cognitive radio systems, enabling the detection and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in particular, have showed promise in recent years for enhancing the precision and effectiveness of spectrum sensing. This paper presents an overview of spectrum sensing using CNNs and RNNs and their performance in cognitive radio systems. Furthermore, the paper delves into the spectrum sensing performance of RNNs, particularly for processing time-series data. RNNs are capable of capturing temporal dependencies in sequential data, which is essential for spectrum sensing tasks where signals evolve over time. Further, the parakeets such as probability of detection (Pd), Probability of false alarm (PFA), Bite Error rate (BER) and Power spectral density (PSD) are analysed for spectrum sensing algorithms. RNNs and CNNs, two examples of deep learning methods (DLM), performed better than more traditional approaches.
Journal Title
Ain Shams Engineering Journal
Journal ISSN
20904479
Digital Object Identifier (DOI)
10.1016/j.asej.2023.102505