Modulation Recognition Algorithm of Multipath Channel Signal for Orthogonal Frequency Division Multiplexing

As wireless communication technology rapidly develops, multipath channel effects pose a severe challenge to the effectiveness of orthogonal frequency division multiplexing systems. The signal distortion caused by multipath channel effects seriously affects the performance of the system. The objective of the research is to design a deep learning-based orthogonal frequency division multiplexing system signal modulation recognition method to improve recognition accuracy in multipath channel environments. The study first uses a subspace imperceptive channel estimation algorithm to perform preliminary processing on the received signal, eliminating the influence of some multipath effects. Then, the minimum mean square error equalization algorithm is used to further balance the signal and reduce signal distortion. Finally, an improved convolutional neural network is used to construct a classifier for modulation recognition of preprocessed signals, thereby establishing an intelligent signal modulation recognition method. The results denoted that the average recognition accuracy of the research algorithm for six modulation modes, namely 16QAM, 32QAM, 64QAM, BPSK, QPSK, and 8PSK, was 0.931, and the recognition accuracy was higher at 10SNR. The testing time was <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.61\times 10^{-4}$ </tex-math></inline-formula>s, and the practicality and modulation recognition accuracy were better than other methods. The research results provide a new solution for signal modulation recognition in orthogonal frequency division multiplexing systems, which can effectively improve the overall performance of communication systems, reduce error rates, and enhance the system’s anti-interference ability.