Deep Learning-Based Channel Estimation

Soltani, Mehran; Pourahmadi, Vahid; Mirzaei, Ali Akbar; Sheikhzadeh, Hamid

doi:10.1109/lcomm.2019.2898944

Cited by 442 publications

(226 citation statements)

References 13 publications

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“…In the context of wideband channels, [35] models the channel time-frequency response as an image, and the pilot based samples as a low-resolution sampled version of the image. The authors use convolutional neural networks (CNN) based image super-resolution and image restoration techniques to estimate the channel, with the mean squared error (MSE) as the loss function.…”

Section: A Channel Estimationmentioning

confidence: 99%

Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

185

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Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

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Section: A Channel Estimationmentioning

confidence: 99%

Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

185

View full text Add to dashboard Cite

show abstract

“…Machine learning/ deep learning can be used to bypass this issue. For instance, The authors of [2]- [9] proposed deep learning for channel estimation. Deep learning can be used also for symbol detection in MIMO systems as proposed in [10], [11] as it can be used for mapping channels in space and frequency as shown in [12].…”

Section: A Massive Mimo and Beamformingmentioning

confidence: 99%

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Arjoune

Faruque

2020

2020 10th Annual Computing and Communication Workshop and Conference (CCWC)

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The advent of the wireless communications systems augurs new cutting-edge technologies, including self-driving vehicles, unmanned aerial systems, autonomous robots, the Internet of-Things, and virtual reality. These technologies require high data rates, ultra-low latency, and high reliability, all of which are promised by the fifth generation of wireless communication systems (5G). Many research groups state that 5G cannot meet its demands without artificial intelligence (AI) integration as 5G wireless networks are expected to generate unprecedented traffic giving wireless research designers access to big data that can help in predicting the demands and adjust cell designs to meet the users' requirements. Subsequently, many researchers applied AI in many aspects of 5G wireless communication design including radio resource allocation, network management, and cyber-security. In this paper, we provide an in-depth review of AI for 5G wireless communication systems. In this respect, the aim of this paper is to survey AI in 5G wireless communication systems by discussing many case studies and the associated challenges, and shedding new light on future research directions for leveraging AI in 5G wireless communications.

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“…For the applications of DL in the communication systems with the block structure, the DL approaches are usually model-driven, and are combined with the communication domain knowledge [12]. DL has been applied to refine the conventional block-structure communications, including the modulation recognition [13], the channel estimation and detection [14][15][16], and the channel decoding [17,18]. The data-driven DL approaches in physical communications treat the entire communication system as an end-to-end reconstruction task.…”

Section: Related Workmentioning

confidence: 99%

Harnessing the Adversarial Perturbation to Enhance Security in the Autoencoder-Based Communication System

Deng

Sang

2020

Electronics

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Given the vulnerability of deep neural network to adversarial attacks, the application of deep learning in the wireless physical layer arouses comprehensive security concerns. In this paper, we consider an autoencoder-based communication system with a full-duplex (FD) legitimate receiver and an external eavesdropper. It is assumed that the system is trained from end-to-end based on the concepts of autoencoder. The FD legitimate receiver transmits a well-designed adversary perturbation signal to jam the eavesdropper while receiving information simultaneously. To defend the self-perturbation from the loop-back channel, the legitimate receiver is re-trained with the adversarial training method. The simulation results show that with the scheme proposed in this paper, the block-error-rate (BLER) of the legitimate receiver almost remains unaffected while the BLER of the eavesdropper is increased by orders of magnitude. This ensures reliable and secure transmission between the transmitter and the legitimate receiver.

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Deep Learning-Based Channel Estimation

Cited by 442 publications

References 13 publications

Machine Learning in the Air

Machine Learning in the Air

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Harnessing the Adversarial Perturbation to Enhance Security in the Autoencoder-Based Communication System

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