Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

Yang, Yang; Li, Yang; Zhang, Wuxiong; Qin, Fei; Zhu, Pengcheng; Wang, Cheng-Xiang

doi:10.1109/mcom.2019.1800635

Cited by 112 publications

(59 citation statements)

References 11 publications

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“…Using GAN can minimize the amount of outdoor collected data required to train the learning models for the RF/FSO ML-based switching method; in particular, that measurement collection can be time-consuming. A recent study revealed that GAN-based modeling is promising in wireless RF communication [112]. GAN-based modeling can be of interest in FSO when accurate stochastic channel modeling is not available.…”

Section: B Boosting the Role Of Machine Learningmentioning

confidence: 99%

Retrofitting FSO Systems in Existing RF Infrastructure: A Non-Zero-Sum Game Technology

Trichili¹,

Ragheb

Briantcev

et al. 2021

IEEE Open J. Commun. Soc.

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Progress in optical wireless communication (OWC) has unleashed the potential to transmit data in an ultrafast manner without incurring large investments and bulk infrastructure. OWC includes wireless data transmissions in three optical sub-bands; ultraviolet, visible, and infrared. This paper discusses installing infrared OWC, known as free space optics (FSO), systems on top of installed radio frequency (RF) networks for outdoor applications to benefit from the reliability of RF links and the unlicensed broad optical spectrum, and the large data rates carried by laser beams propagating in free space. We equally review commercially available solutions and the hardware requirements for RF and FSO technology co-existence. The potential of hybrid RF/FSO for space communication is further discussed. Finally, open problems and future research directions are presented.

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Section: B Boosting the Role Of Machine Learningmentioning

confidence: 99%

Retrofitting FSO Systems in Existing RF Infrastructure: A Non-Zero-Sum Game Technology

Trichili¹,

Ragheb

Briantcev

et al. 2021

IEEE Open J. Commun. Soc.

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“…For that purpose, the generator and the discriminator of the GAN need to be distributed to different locations. Those aspects were missing in the past applications of GANs to model wireless communication channels, e.g., [50]- [52], where the GAN is centrally trained offline by accounting for waveform effects over a single channel only (without distinguishing the roles of the transmitter and the receiver, and their relative channel effects).…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

Shi

Davaslıoğlu

Sagduyu

2021

IEEE Trans. Cogn. Commun. Netw.

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The spoofing attack is critical to bypass physicallayer signal authentication. This paper presents a deep learningbased spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended transmissions. The adversary is modeled as a pair of a transmitter and a receiver that build the generator and discriminator of the generative adversarial network, respectively, by playing a minimax game over the air. The adversary transmitter trains a deep neural network to generate the best spoofing signals and fool the best defense trained as another deep neural network at the adversary receiver. Each node (defender or adversary) may have multiple transmitter or receiver antennas. Signals are spoofed by jointly capturing waveform, channel, and radio hardware effects that are inherent to wireless signals under attack. Compared with spoofing attacks using random or replayed signals, the proposed attack increases the probability of misclassifying spoofing signals as intended signals for different network topology and mobility patterns. The adversary transmitter can increase the spoofing attack success by using multiple antennas, while the attack success decreases when the defender receiver uses multiple antennas. For practical deployment, the attack implementation on embedded platforms demonstrates the low latency of generating or classifying spoofing signals. Index Terms-Adversarial machine learning, deep learning, generative adversarial network (GAN), spoofing attack.

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“…One way to overcome this is to conduct a synthesis of artificial data using generative adversarial neural networks as pointed out in [108]. Roughly speaking, this open challenge is a formidable task, since conducting such synthesis could potentially introduce unwanted bias to existing data, even though for specific applications a number of suitable examples of this method can be found in the literature, such as wireless channel modeling [109], [110]. 6) The traditional approach to measure interference is mainly conducted through SNR or RSSI measurement data, which strictly relies on the data collection at certain intervals, and communication established from other nodes is mainly treated as a background noise for the sake of simplicity.…”

Section: B Future Research Directionsmentioning

confidence: 99%

Machine Learning for Wireless Link Quality Estimation: A Survey

Cerar

Yetgin

Mohorčič

et al. 2021

IEEE Commun. Surv. Tutorials

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Since the emergence of wireless communication networks, a plethora of research papers focus their attention on the quality aspects of wireless links. The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling link quality estimation are becoming increasingly sophisticated. A number of recent estimators leverage Machine Learning (ML) techniques that require a sophisticated design and development process, each of which has a great potential to significantly affect the overall model performance. In this paper, we provide a comprehensive survey on link quality estimators developed from empirical data and then focus on the subset that use ML algorithms. We analyze ML-based Link Quality Estimation (LQE) models from two perspectives using performance data. Firstly, we focus on how they address quality requirements that are important from the perspective of the applications they serve. Secondly, we analyze how they approach the standard design steps commonly used in the ML community. Having analyzed the scientific body of the survey, we review existing open source datasets suitable for LQE research. Finally, we round up our survey with the lessons learned and design guidelines for MLbased LQE development and dataset collection.

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Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

Cited by 112 publications

References 11 publications

Retrofitting FSO Systems in Existing RF Infrastructure: A Non-Zero-Sum Game Technology

Retrofitting FSO Systems in Existing RF Infrastructure: A Non-Zero-Sum Game Technology

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

Machine Learning for Wireless Link Quality Estimation: A Survey

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