Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

Abstract

In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as 2 × N size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as 4 × N size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from 4 × N to 2 × N by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range. INDEX TERMSAutomatic modulation classification, Deep learning model, Convolution neural network, Frame extension, Cognitive radio.

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

“…Zheng et al [19] compared three fussion methods for handling variation in the length of the signal passed as input to the CNN classifier. In [20], a CNN classifier was trained with reduced signal dimensions and was shown to outperform some ML-based models. Lin et al [21] proposed a combination of gated recurrent unit and CNN to detect temporal features.…”

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“…Inspired by ECNN [10], before training the module, the whole dataset S is extended as 2 × 2N by copying data and concatenating in reverse order to improve the recognition accuracy:…”

“…MCBL is trained from scratch with randomly initialized weights using adaptive moment (Adam) optimizer. The [37], CLDNN [19], VT_CNN2 [18], LSTM [38], ResNet [39], VGG [40], CNN_LSTM [41], and two latest models ECNN [10] and CGDNet [9]. All of these algorithms use the same dataset without any preprocessing.…”

“…To enable a fully automatic feature extraction, several deep neural network-(DNN-) based proposals have been put forward recently, including back propagation (BP) neural network [6], convolutional neural network (CNN) [7], long short-term Memory (LSTM) [8], CGDNet [9], and ECNN [10]. However, extensive experiments on the open dataset [11] have shown that the recognition accuracy of existing proposals is still unsatisfactory (in Section III) since they cannot fully capture the temporal-spatial characteristics of the signals.…”

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