Deep Learning-Based Signal Modulation Identification in OFDM Systems

IEEE Access

Hao Gu¹  Guan Gui¹  Yu Wang¹  Yibin Zhang¹  Sheng Hong¹  Hikmet Sari¹ 

[1] College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China;

关键词: Orthogonal frequency-division multiplexing (OFDM);    deep learning (DL);    signal modulation identification (SMI);    convolutional neural network (CNN);   

DOI  :  10.1109/ACCESS.2019.2934976

来源: DOAJ

【 摘 要 】

Signal modulation identification (SMI) plays a very important role in orthogonal frequency-division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via feature extraction based on machine learning. However, the traditional methods encounter a bottleneck where the probability of correct classification (PCC) is very limited and hence it is hard to implement in practical OFDM systems due to the fact that traditional methods are difficult to extract feature of the OFDM signals. In order solve these problems, we propose a deep learning (DL) based SMI method for identifying OFDM signals. Specifically, convolutional neural network (CNN) is adopted to train in-phase and quadrature (IQ) samples for OFDM signals. Then we choose dropout layer to prevent overfitting and improve its identification accuracy. In addition, datasets with different modulation modes are adopted to verify our trained CNN. Experiments are conducted to show that our proposed method achieves higher accuracy and better consistency than traditional methods. Moreover, extensive results confirm that the proposed method performs robustly in different datasets.

【 授权许可】

Unknown