The Rayleigh Fading Channel Prediction via Deep Learning

Liao, Runfa; Wen, Hong; Wu, Jinsong; Song, Huanhuan; Pan, Fei; Liang, Dong

doi:10.1155/2018/6497340

Cited by 62 publications

(34 citation statements)

References 24 publications

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“…[451] Learning to communicate over an impaired channel AE + radio transformer network Ye et al [452] Channel estimation and signal detection in OFDM systsms. MLP Liang et al [453] Channel decoding CNN Lyu et al [454] NNs for channel decoding MLP, CNN and RNN Dörner et al [455] Over-the-air communications system AE Liao et al [456] Rayleigh fading channel prediction MLP Huang et al [458] Light-emitting diode (LED) visible light downlink error correction AE Alkhateeb et al [444] Coordinated beamforming for highly-mobile millimeter wave systems MLP Gante et al [443] Millimeter wave positioning CNN Ye et al [506] Channel agnostic end-to-end learning based communication system Conditional GAN architecture achieves near-optimal accuracy, while requiring light computation without prior knowledge of Signal-to-Noise Ratio (SNR). Yan et al employ deep learning to solve a similar problem from a different perspective [447].…”

Section: Deep Learning Driven Signal Processingmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,335

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: Deep Learning Driven Signal Processingmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,335

838

View full text Add to dashboard Cite

show abstract

“…On the issue of fading channel prediction, standard predictive neural networks with full connections have been evaluated in various contexts in single-subcarrier settings, e.g. Ding and Hirose (2014) [21], Liao et al (2018) [22], Jiang andSchotten (2019, 2020) [23], [24], and Yuan et al (2020) [25], with [25] employing an extra CNN channel classifier to identify patterns in the autocorrelation function of the channel prior to applying the actual predictor. Overall, while the Kalman filtering scheme is based on linear models and parametrised probability assumptions, predictive neural networks incorporate nonlinearities and no specific interpretation (such as Kalman gain or conditional state variance) is imposed on their model parameters.…”

Section: A Background and Related Workmentioning

confidence: 99%

Convolutional-Type Neural Networks for Fading Channel Forecasting

Ahrens

Schotten

2020

IEEE Access

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In this paper, a series of convolutional-type predictive neural networks are proposed for the issue of fading channel forecasting for orthogonal frequency-division multiplexing (OFDM) transmission systems in a multiple-input and multiple-output (MIMO) mode via a noisy channel. The proposed neural networks all employ convolutional connections that operate in a translation-invariant manner in the frequency domain of the time-varying channel transfer function, which effectively tackles the essential challenges of high dimensionality and denoising. Each of the proposed convolutional-type neural networks is built on a specific overall network architecture and functions as an independent predictor that offers advantages regarding a specific aspect such as accuracy over a certain prediction span or computational effort. Comparative evaluations against common prediction methods such as the Kalman filtering scheme and the standard long-short term memory units (LSTMs) are provided on the basis of transmission simulations over dispersive fading channels with Rayleigh components according to the well-established 3GPP Long Term Evolution (LTE) standards. INDEX TERMS Wireless communications, deep learning, convolutional neural networks, LSTMs, time series analysis, fading channel forecasting, MIMO.

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“…The network performance prediction and related deep learning technologies for solving mobile networking problems are summarized in [10], and the case studies and intelligent decision-making use of machine learning are described in [11]. The prediction of channel state information (CSI) was proposed in [10][11][12][13][14]. CSI is predicted by the position of the terminal, temperature, humidity, and weather in [12].…”

Section: Introductionmentioning

confidence: 99%

Using vision-based object detection for link quality prediction in 5.6-GHz channel

Kudo

Takahashi

Inoue

et al. 2020

J Wireless Com Network

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Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

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The Rayleigh Fading Channel Prediction via Deep Learning

Cited by 62 publications

References 24 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

Convolutional-Type Neural Networks for Fading Channel Forecasting

Using vision-based object detection for link quality prediction in 5.6-GHz channel

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