Deep Neural Network for Robust Modulation Classification Under Uncertain Noise Conditions

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 1; pp. 564 - 577
• Main Authors: Hu, Shisheng, Pei, Yiyang, Liang, Paul Pu, Liang, Ying-Chang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blind Modulation Classification; Channel estimation; Classification; Classifiers; Cognitive Radio; Complexity; Computer simulation; Cost analysis; Deep Neural Network; Feature extraction; Gaussian noise; Maximization; Maximum likelihood estimation; Modulation; Neural networks; Noise; Optimization; Parameter estimation; Recurrent neural networks; Robustness; Signal distortion; Signal processing; SISO (control systems); Training
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2951594

Recently, classifying the modulation schemes of signals using deep neural network has received much attention. In this paper, we introduce a general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO) systems. Its feasibility is analyzed using maximum a posteriori probability (MAP) criterion and its robustness to uncertain noise conditions is compared to that of the conventional maximum likelihood (ML)-based classifiers. To reduce the design and training cost of DNN classifiers, a simple but effective pre-processing method is introduced and adopted. Furthermore, featuring multiple recurrent neural network (RNN) layers, the DNN modulation classifier is realized. Simulation results show that the proposed RNN-based classifier is robust to the uncertain noise conditions, and the performance of it approaches to that of the ideal ML classifier with perfect channel and noise information. Moreover, with a much lower complexity, it outperforms the existing ML-based classifiers, specifically, expectation maximization (EM) and expectation conditional maximization (ECM) classifiers which iteratively estimate channel and noise parameters. In addition, the proposed classifier is shown to be invariant to the signal distortion such as frequency offset. Furthermore, the adopted pre-processing method is shown to accelerate the training process of our proposed classifier, thus reducing the training cost. Lastly, the computational complexity of our proposed classifier is analyzed and compared to other traditional ones, which further demonstrates its overall advantage.