Frequency Domain Analysis and Convolutional Neural Network Based Modulation Signal Classification Method in OFDM System

Frequency Domain Analysis and Convolutional Neural Network Based Modulation Signal Classification Method in OFDM System

연구 분야: Infrastructure

학회: 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)

Automatic Modulation Classification (AMC) is widely used in many aspects and occupies a critical position in non-cooperative communication. Recently, deep learning (DL) based AMC algorithms attract more and more attention due to the outstanding performance in modulation recognition. In this paper, we propose a novel convolutional neural network (CNN)-based AMC method that employs frequency domain analysis (FDA) pre-processing and l_{2} regularization for the orthogonal frequency division multiplexing (OFDM) systems. Different from traditional algorithms, the proposed algorithm is superior in high accuracy of the classification even at low signal-to-noise ratios (SNRs) owing to pre-processing by FFT. Moreover, the adoption of l_{2} regularization effectively suppresses overfitting. Simulation results are given to illustrate that our proposed method has an evidently advantage over traditional methods in classifying BPSK, 4PSK, 8PSK and 16QAM modulation techniques.