A Survey on Deep Learning Techniques in Wireless Signal Recognition

Li, Xiaofan; Dong, Fangwei; Zhou, Sha; Guo, Weibin

doi:10.1155/2019/5629572

Cited by 112 publications

(69 citation statements)

References 89 publications

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“…Meanwhile, wireless networks are generating a massive volume of data, from which we can extract useful information using the state-of-the-art machine learning (ML) techniques [21]- [24]. There have been some efforts exploring the applications of ML in DoA estimation.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems

Cao

Lin

et al. 2020

IEEE Trans. Veh. Technol.

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The near-field effect of short-range multiple-input multiple-output (MIMO) systems imposes many challenges on direction-of-arrival (DoA) estimation. Most conventional scenarios assume that the far-field planar wavefronts hold. In this paper, we investigate the DoA estimation problem in short-range MIMO communications, where the effect of near-field spherical wave is non-negligible. By converting it into a regression task, a novel DoA estimation framework based on complex-valued deep learning (CVDL) is proposed for the near-field region in shortrange MIMO communication systems. Under the assumption of a spherical wave model, the array steering vector is determined by both the distance and the direction. However, solving this regression task containing a massive number of variables is challenging, since datasets need to capture numerous complicated feature representations. To overcome this, a virtual covariance matrix (VCM) based on received signals is constructed, and thus such features extracted from the VCM can deal with the complicated coupling relationship between the direction and the distance. Although the emergence of wireless big data driven by future communication networks promotes deep learning-based wireless signal processing, the learning algorithms of complexvalued signals are still ongoing. This paper proposes a onedimensional (1-D) residual network that can directly tackle complex-valued features due to the inherent 1-D structure of signal subspace vectors. In addition, we put forth a cropped VCM based policy which can be applied to different antenna sizes. The proposed method is able to fully exploit the complex-valued information. Our simulation results demonstrate the superiority of the proposed CVDL approach over the baseline schemes in terms of the accuracy of DoA estimation.

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Section: A Related Work and Motivationmentioning

confidence: 99%

Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems

Cao

Lin

et al. 2020

IEEE Trans. Veh. Technol.

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“…After the up-sampling process, we used convolutional layers of two classes followed by a softmax activation function. Then, the cross-entropy loss function is used according to equation (11) to calculate the minimum loss between the input and the detected data bits. The two proposed models received the same modulated OOK data after passing through turbulence channels with AWGN, and the output of these models is a vector of data bits recovered through the cross-entropy loss function.…”

Section: B Proposed DL Detection Modelsmentioning

confidence: 99%

“…Recently, researchers have widely demonstrated DL in many areas, such as in computer vision and speech recognition [7][8]. They have also succeeded in applying DL in different areas of wireless communication systems, for encoding, decoding, modulation recognition, and channel estimation [9][10][11]. In [12], researchers proposed the use of a DL autoencoder to replace both the transmitter and the receiver of the communication system.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Improving Performance of OOK Modulation Over FSO Turbulent Channels

Darwesh

Kopeika

2020

IEEE Access

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Free space optical (FSO) communication technology has become increasingly advanced with capabilities of high speed, high capacity, and low power consumption. However, despite the great potential of FSO, its performance is limited in a turbulent atmosphere. Atmospheric turbulence causes scintillation in the FSO propagated signals, leading to an increase in the bit error rate (BER) performance of the recovered signals at the receiver. In this paper, we demonstrate that the use of deep learning (DL) detection methods could overcome these limitations. We present a new detection method of on-off keying (OOK) modulated signals by using different models of DL over different strength FSO turbulent channels, without the need for prior knowledge of the parameters of the channel. The demonstrated DL decoders improve the performance of the FSO turbulent channel and decrease the power consumption. Moreover, the demonstrated DL models also work faster than maximum likelihood (ML) methods with perfect channel estimation decoders, with even slightly better performance because of the turbulence, thus enabling realization of FSO over turbulent atmospheric channels. INDEX TERMS Free space optical communication, deep learning, on-off keying modulation, amplitude shift keying modulation, maximum likelihood, channel state information, fully convolutional neural network, fully connected neural network, additive white gaussian noise, intensity modulation, direct detection, photodetector, bit error rate.

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“…These methods have dramatically improved the state of the art in many domains, demonstrating better generalization, scalability and robustness capabilities than previous solutions [17]. As a result, in recent years, many researchers are exploring the application of DL methods to the AMC field, obtaining promising results [5], [18].…”

Section: Related Workmentioning

confidence: 99%

Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices

et al. 2020

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The continuous increase in the number of mobile and Internet of Things (IoT) devices, as well as in the wireless data traffic they generate, represents an essential challenge in terms of spectral coexistence. As a result, these devices are now expected to make efficient and dynamic use of the spectrum by employing Cognitive Radio (CR) techniques. In this work, we focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy-and computation-constrained end-devices. In this work, we propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices. We trained and tested our solution with over-the-air measurements of real radio signals. Our results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%.

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A Survey on Deep Learning Techniques in Wireless Signal Recognition

Cited by 112 publications

References 89 publications

Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems

Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems

Deep Learning for Improving Performance of OOK Modulation Over FSO Turbulent Channels

Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices

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