Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

Abstract

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

A Survey on Deep Learning Techniques in Wireless Signal Recognition

“…Hidden layers maybe one layer or multilayer, and each layer consists of several nodes. The [26,37] (ii) KNN [38,91] (iii) SVM [6,27,47,48,92] (iv) Naïve Bayes [39] (v) HMM [46] (vi) Fuzzy classifier [93] (vii) Polynomial classifier [40,94] (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 73-76, 79, 81, 82, 95, 96] (iv) LSTM [29,69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (ix) NFSC [78] Pros (i) works better on small data (ii) low implementation cost (i) simple pre-processing (ii) high accuracy and efficiency (iii) adaptive to different applications Cons (i) time demanding (ii) complex feature engineering (iii) depends heavily on the representation of the data (iv) prone to curse of dimensionality (i) demanding large amounts of data (ii) high hardware cost node presented in Figure 3 is the basic operational unit, in which the input vector is multiplied by a series of weights and the sum value is fed into the activation function . These operational units contribute to a powerful network, which could realize complex functions such as regression and classification.…”

“…To further improve the performance of CNN-based recognition schemes in [10], the authors present a signal distortion correction module (CM) and results show that this CM+CNN scheme achieves better accuracy than the existing schemes. In [71,72], a heterogeneous deep model fusion (HDMF) approach is proposed, and the two different combinations between CNN and LSTM network without prior information are discussed. The timedomain data are delivered to fusion model without additional operations.…”

“…Wireless technology recognition Algorithms (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 95] (iv) LSTM [69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (i) CNN [73-76, 79, 81, 82, 96] (ii) LSTM [29] (iii) NFSC [78] [74], the amplitude and phase difference representation were employed for CNN training procedure. The results indicate that the recognition of radar signals has been realized successfully with the proposed scheme even under the condition of LTE and WLAN signals coexisting at the same time.…”

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

“…Background. Deep learning has been proven to be a powerful tool for pattern classification problems and sensor studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A deep learning model usually has more than three layers, and by using multiple layers, the model extracts hierarchical features from the original data.…”

Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

“…Machine learning techniques have been very recently explored at the physical layer of wireless communications [10]- [13]. For example, [11] developed a deep-learning (DL) autoencoder for single-input multiple-output (SIMO) communication systems with deep neural networks (DNNs).…”