Deep Learning-Based Automatic Modulation Classification With Blind OFDM Parameter Estimation
نویسندگان
چکیده
Automatic modulation classification (AMC) is an essential factor in dynamic spectrum access to fulfill the demand of 5G wireless communications for achieving high data rate and low latency. Many deep learning (DL)-based AMC methods have achieved improved accuracy classifying analog schemes, single-carrier-based multi-carrier signals using several DL architectures such as convolutional neural network (CNN) long-short term memory (LSTM). However, most conventional DL-based confused orthogonal frequency multiplexing division (OFDM)-based with different OFDM useful symbol lengths. To resolve issue, we propose a CNN model operating on fast Fourier transformation window bank (FWB) extract length OFDM, which represents identification each OFDM-based communication technology. After extracting length, system combined FWB in-phase quadrature-phase classify single-carrier schemes simultaneously. Furthermore, explore constraints parameters according (FFT) size signal achieve good through experiment. We constructed dataset by generating lengths while changing FFT fixed bandwidth selecting only quadrature amplitude (QAM) from RadioML2016.10a. Experimental results show about 30% over classifiers additive white Gaussian noise, synchronization impairments, fading environments.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3102223