Combinatorial neural network signal modulation recognition algorithm based on attention mechanism
Yuanyuan Zhang, Ming-Feng Lu, Yuxiang Wang · 2023
Aiming at the problems of low recognition rate and confused signal classification of deep learning modulation recognition network, combined neural network of one-dimensional residual network and long short-term memory based on efficient channel attention (ECA-RLDNet) is proposed. The algorithm designs a one-dimensional efficient channel attention mechanism to connect two feature extraction network units, uses the one-dimensional residual network to extract signal time series features, the attention mechanism gives higher weight to the key information of signal features, and further uses the long short-term memory to extract time series association features to obtain comprehensive and effective feature information. By simulating the modulation signal dataset under non-ideal channel and experimenting with the algorithm, the experimental results indicate that the highest recognition accuracy of ECA-RLDNet reaches 92.32%, which reduces the probability of confusion of high-order digital modulated signals.