A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

Mao, Bomin; Tang, Fengxiao; Fadlullah, Zubair Md.; Kato, Nei; Akashi, Osamu; Inoue, Takeru; Mizutani, Kimihiro

doi:10.1109/mwc.2018.1700417

Cited by 111 publications

(39 citation statements)

References 13 publications

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“…In recent years, deep learning (DL) is considered as a powerful tool, because it is expert in automatic feature extraction from huge amounts of data, instead of the complex and difficult design of man-made features [21]- [24]. For this reason, DL has been successfully applied in network traffic prediction [25]- [28], physical layer wireless techniques [30]- [35] and internet-of-things [36]- [40]. In addition, DL has been applied in multiple input and multiple output (MI-MO) [41], non-orthogonal multiple access (NOMA), and cognitive radio (CR).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

2020

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In the scenarios of non-cooperative wireless communications, automatic modulation recognition (AMR) is an indispensable algorithm to recognize various types of signal modulations before demodulation in many internet of things applications. Convolutional neural network (CNN)-based AMR is considered as one of the most promising methods to achieve good recognition performance. However, conventional CNN-based methods are often unstable and also lack of generalized capabilities under varying noise conditions, because these methods are merely trained on specific dataset and can only work at the corresponding noise condition. Hence, it is hard to apply these methods directly in practical systems. In this paper, we propose a CNN-based robust automatic modulation recognition (RAMR) method to recognize three types of modulation signals, i.e., frequency shift key (FSK), phase shift key (PSK), and quadrature amplitude modulation (QAM). The proposed method is trained on a mixed dataset for extracting common features under varying noise scenarios. Simulation results show that our proposed generalized CNN-based architecture can achieve higher robustness and convenience than conventional ones. INDEX TERMS Automatic modulation recognition, deep learning, convolutional neural network, internet of things.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

2020

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“…In the network layer, DL has been applied into network traffic control [23]- [28], predication [29]- [33] and classification [34], [35]. For instance, paper [23] and paper [24] proposed two kinds of DL-based network traffic control methods, respectively. The former one based on supervised convolutional neural network (CNN), and its achieved lower delay and packet loss rate than these existing routing methods; The latter one is a novel non-supervised methods, and it also outperformed conventional routing protocols.…”

Section: Introductionmentioning

confidence: 99%

Device Type Identification via Network Traffic and Lightweight Convolutional Neural Network for Internet of Things

et al. 2020

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Device type identification (DTI) is one of the most important techniques for the management of Internet of things (IoT). Recently, deep learning (DL) has been considered as a powerful tools for classification or identification, and some researches have introduced DL into DTI for advanced performance. However, DL-based DTI generally has high computational complexity and large model sizes, which are unsuitable for IoT. Thus, we proposed a lightweight convolutional neural network-based DTI with low computational complexity and few model sizes. In detail, we remove the redundant fully-connected layers and replace common convolution with separable convolution. Simulation results demonstrate that lightweight CNN-based DTI just has less than 5% of computational complexity and model size of CNNbased DTI, though there are less than 0.5% performance gap between them. INDEX TERMS Lightweight convolutional neural network, network traffic, device type identification (DTI), internet of things (IoT).

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Section: Introductionmentioning

confidence: 99%

Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

Wang¹,

Gui²,

Ohtsuki³

et al. 2020

Preprint

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Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

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A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

Cited by 111 publications

References 13 publications

Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

Device Type Identification via Network Traffic and Lightweight Convolutional Neural Network for Internet of Things

Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

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