Radio Classify Generative Adversarial Networks: A Semi-supervised Method for Modulation Recognition

Li, Mingxuan; Li, Shuntao; Wu, Yifan

doi:10.1109/icct.2018.8600032

Cited by 18 publications

(11 citation statements)

References 3 publications

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“…We showed that the amplitude/phase representation outperformed the other two, demonstrating the importance of the choice of the wireless data representation used as input to the deep learning technique so as to determine the most optimal mapping from the raw signal to the modulation scheme. Other, follow-up works include [154][155][156][157][158]162,163,[165][166][167][168][169], etc.…”

Section: Radio Spectrum Analysismentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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Section: Radio Spectrum Analysismentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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“…In this direction, several researcher have focused on presenting ML-related solutions for automatic modulation recognition (AMR) (Khan et al, 2016;Li et al, 2018;Wu et al, 2018;Iqbal et al, 2019;Shah et al, 2019;Yang et al, 2019;Bu et al, 2020), channel estimation (Satyanarayana et al, 2019;Zhu et al, 2019;Liu S. et al, 2020;Ma et al, 2020a;Ma et al, 2020b;Moon et al, 2020;Mai et al, 2021;Wang et al, 2021), and signal detection (Jeon et al, 2018;Aoudia and Hoydis, 2019;Samuel et al, 2019;Katla et al, 2020;Satyanarayana et al, 2020). In more detail, AMR has been identified as an important task for several wireless systems, since it enables dynamic spectrum access, interference monitoring, radio fault self-detection as well as other civil, government, and military applications.…”

Section: Phy Layermentioning

confidence: 99%

“…Additionally, in (Li et al, 2018), a semi-supervised deep convolutional generative adversarial network (GAN) was presented that consists of a pair of GANs that collaboratively create a powerful modulator discriminator. The ML network receives as inputs the I/Q components of a number of received signal samples and matches them to a set of modulation formats.…”

Section: Phy Layermentioning

confidence: 99%

“…It is worth mentioning that, for the sake of competence, Table 2 includes some contributions that although do not refer to THz wireless systems, they can be straightforwardly applied to them. For example, (Shah et al, 2019;Yang et al, 2019) and (Li et al, 2018) can be applied to any system that employ I/Q modulation and demodulations approaches, whereas (Khan et al, 2016) is suitable for the ones that use coherent RXs. Of note, according to (Boulogeorgos et al, 2018b), coherence RXs are a usual choice in THz wireless fiber extenders.…”

Section: Phy Layermentioning

confidence: 99%

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Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

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With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.

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“…We showed that the amplitude/phase representation outperformed the other two, demonstrating the importance of the choice of the wireless data representation used as input to the deep learning technique so as to determine the most optimal mapping from the raw signal to the modulation scheme. Other, follow-up works include [157], [158], [159], [160], [161], [165], [166], [168], [169], [170], [171], [172], etc.…”

Section: Activity Recognitionmentioning

confidence: 99%

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

Kulin¹,

Kazaz²,

Moerman³

et al. 2020

Preprint

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This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

show abstract

Radio Classify Generative Adversarial Networks: A Semi-supervised Method for Modulation Recognition

Cited by 18 publications

References 3 publications

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Machine Learning: A Catalyst for THz Wireless Networks

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

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