DO-CAB algorithm for Bi-LSTM neural network signal recognition
To address the problem of insufficient recognition accuracy for uplink signals in two-way automatic communication systems(TWACS),a signal recognition algorithm based on the dandelion optimization(DO)algorithm that combines convolutional neural network(CNN)with attention mechanism(AM)and bidirectional long short-term memory(Bi-LSTM)neural networks is proposed,which is briefly referred to as the DO-CAB algorithm.The algorithm first adaptively extracts important features of TWACS signals using a CNN.It then optimizes the hyperparameters of the Bi-LSTM using the DO algorithm,constructs the net-work based on the optimized hyperparameters,and introduces an AM to assign influence weights to the inputs,improving the net-work algorithm for better signal recognition.The experimental results show that the proposed algorithm achieves a recognition accuracy of 92.32％,enabling efficient and accurate identification of TWACS modulated signals.
two-way automatic communication systemsdandelion optimization algorithmbidirectional long short-term memo-ry networkdeep learningsignal detection
国家科技期刊平台
NSTL
万方数据
维普
