… propose a CNN-LSTM detector which first uses the CNN to … sensing periods are input
into the LSTM so that the PU activity … With sufficient simulations, the superiority of the CNN-LSTM …

… CNNLSTM, detecting the state of PU activity through the received signal, using parallel CNN
network and LSTM … use of the advantages of CNN network and LSTM network, and solving …

… hand, LSTM uses … LSTM-CNN model to extract temporal as well as spatial data from the
incoming signal. According to simulation results, LSTM-CNN outperforms CNN and the LSTM …

… As a key technology of cognitive radio, spectrum sensing has an irreplaceable position. In
this paper, we proposed a parallel CNN-LSTM network based deep learning algorithms for …

… show better detection performance as compared to CNN, LSTM, and DetectNet. However,
the … The proposed model attained the least Pf , followed by LSTM, CNN, and DetectNet. It can …

… such Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) do not …
LSTM classifier for recognizing the modulation scheme of a received signal by a CR. The LSTM …

… To overcome the limitations about the traditional CNN and LSTM, we propose a deep … of a
forward LSTM layer and a backward LSTM layer, we begin with a standard LSTM network. As …

… In this paper, we propose an LSTM based spectrum sensing (LSTM-… LSTM-SS scheme,
followed by CNN and ANN. This is because LSTM exploits the temporal dependency while CNN …

… The multi-channel spectrum sensing method of cognitive radio based on CNN joint LSTM
network model involves the field of radio monitoring and spectrum management. …

… networks (CNN) and long short-term memory (LSTM). A hybrid mix of CNN and LSTM [12–18…
, which combines the best features of both LSTM and extreme learning machines (ELM), for …