… a deep learning based … deep learning based cooperative detection system is proposed,
which is shown to provide substantial performance gain over conventional cooperative sensing …

… We present spectrum sensing as a classification problem and propose a … In this paper, we
use deep learning for spectrum sensing. By formulating spectrum sensing as a classification …

… spectrum sensing using data from multiple sensing periods and the LSTM is used to learn
the … on spectrum sensing with samples from a single sensing period and the LSTM is used for …

… First, we develop a new LSTM based deep learning solution using time domain amplitude …
use of deep learning models for technology classification task in a distributed sensor network …

… sensing task of Orthogonal Frequency Division Multiplexing (OFDM) signals. For the spectrum
sensing … paper introduces a deep learning method into the spectrum sensing field to seek …

… minimizes the margin of error in the detection of the free spectrum. This research provides
an insight into using a deep neural network for spectrum sensing. A deep learning based …

… deep learning algorithm for spectrum sensing. Compared with model-based spectrum sensing
algorithms, our proposed deep learning … present sensing data and historical sensing data, …

… This article presents a deep learning model for spectrum sensing. One-dimensional
convolutional neural network (1D CNN) and bidirectional long short-term memory (BiLSTM) network …

… degrade the sensing accuracy. To … sensing frameworks drawing support from deep
learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing …

… In this study, we provide a tutorial for deep learning-based spectrum sensing and signal
classification systems. For example, SCF provides the highest accuracy among features; …