In cognitive radio network, the licensed spectrum for the primary user can be accessed in an opportunistic manner by secondary user, or unlicensed user. 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 spectrum sensing. As much modulated signals and noise data as possible are generated to train the model to accommodate detection of multiple types signal. Various experiments are performed to prove the effectiveness of proposed method, and requiring no prior knowledge about the information of licensed user or channel state. The simulation results show that the model can detect multiple modulation types under a large scale of SNRs, especially in low SNR.
Spectrum Sensing Based on Parallel CNN-LSTM Network
2020-05-01
150884 byte
Conference paper
Electronic Resource
English
Vehicle trajectory prediction based on LSTM network
IEEE | 2022
|LSTM LSTM-based future threat prediction method and apparatus
European Patent Office | 2021
|LSTM LSTM-based steering behavior monitoring device and its method
European Patent Office | 2020
Smartphone Heading Correction Method Based on LSTM Neural Network
Springer Verlag | 2022
|