AUTOMATIC MODULATION CLASSIFIER: REVIEW

The automatic modulation classification (AMC) is highly important to develop intelligent receivers in different military and civilian applications including signal intelligence, spectrum management, surveillance, signal confirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowing much information related to transmitted data and various indefinite parameters at receiver, like timing information, carrier frequency, signal power, phase offsets, and so on, the modulation’s blind identification has been a hard task in the real world situations with multi-path fading, frequency-selective in addition to the time-varying channels. There are 2 methods could be utilized to decide the classification signal technique: Feature-based (FB) approach and the Maximum likelihood functions (LB) method. With regard to the FB (referred to as pattern-recognition) classification method used in the study. In the presented work, thorough study is provided to find easy method to identify and classify the digital modulation signals at low SNRs. Spectral-based features, high-order statistic features, wavelet-based features, also cyclic features on the basis of cyclostationary typically utilized to determine and discriminate modulation types have been examined. The number of the classifiers which have been utilized in the process of discrimination have been studied thoroughly and compared for helping researchers in determining and finding the drawbacks with pattern-recognition according to past works. The presented study serving as guide with regard to studies of AMC for determining adequate algorithms and features.

Automatic Digital Modulation Classification Based on Curriculum Learning

Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with −18–20 dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare.

Implementation of Deep Learning-based Automatic Modulation Classifier on FPGA SDR Platform

Intelligent radios collect information by sensing signals within the radio spectrum, and the automatic modulation recognition (AMR) of signals is one of their most challenging tasks. Although the result of a modulation classification based on a deep neural network is better, the training of the neural network requires complicated calculations and expensive hardware. Therefore, in this paper, we propose a master–slave AMR architecture using the reconfigurability of field-programmable gate arrays (FPGAs). First, we discuss the method of building AMR, by using a stack convolution autoencoder (CAE), and analyze the principles of training and classification. Then, on the basis of the radiofrequency network-on-chip architecture, the constraint conditions of AMR in FPGA are proposed from the aspects of computing optimization and memory access optimization. The experimental results not only demonstrated that AMR-based CAEs worked correctly, but also showed that AMR based on neural networks could be implemented on FPGAs, with the potential for dynamic spectrum allocation and cognitive radio systems.