Deep learning-based direct sequence spread spectrum signal modulation mode recognition and parameter estimation algorithm

Direct sequence spread spectrum (DSSS) signals, due to their complex signal structure, pose challenges for accurate modulation mode recognition and parameter estimation using traditional methods. This paper proposes a deep learning-based DSSS signal mode recognition and parameter estimation algorithm. It employs cyclic spectrum analysis to estimate the carrier frequency and code rate parameters, and combines the feature extraction capabilities of a convolutional neural network (CNN) with the modeling and recognition strengths of a long short-term memory (LSTM) network to achieve high-precision DSSS signal recognition. Experimental results demonstrate that the cyclic spectrum method can accurately estimate the carrier frequency and code rate parameters, and the CNN+LSTM mode recognition achieves an accuracy rate of over 85% even at a signal-to-noise ratio below 0 dB, validating the effectiveness of the proposed algorithm for DSSS signal mode recognition.