Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

Yi, Xuemei; Zhong, Caijun

doi:10.1109/lcomm.2020.3014382

Cited by 68 publications

(28 citation statements)

References 14 publications

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“…Authors of [151]- [159] have considered the time frequency response of wireless channel as a 2D image as in Fig. 21 where LR and HR stand for low resolution and high resolution, respectively.…”

Section: A Transceiver Designmentioning

confidence: 99%

“…A DL-based receiver called ChannelNet including two different CNNs, super-resolution CNN [160] and denoising CNN [161], which improves the resolution of low resolution image and eliminates the effect of noise, respectively, is designed. In [151], inspired from the image super resolution technique [162] and conditioned image synthesis technology [163], two cascaded NNs, channel estimation network and channel conditional recovery network, have been used to estimate the channel and detect the signal.…”

Section: A Transceiver Designmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: A Transceiver Designmentioning

confidence: 99%

Section: A Transceiver Designmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In Fig. 5 (a), we offer the evaluation results of the frequency extrapolation in mmWave band based on the CNN and conventional interpolation scheme [5]. The antenna configuration is the same as that in Fig.…”

Section: A Frequency Extrapolation Within a Frequency Bandmentioning

confidence: 99%

Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions

Zhang

Liu

Gao³

et al. 2021

Preprint

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With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO.In these systems, how to acquire accurate channel state information (CSI) is difficult and becomes a bottleneck of the communication links. In this article, we introduce the concept of channel extrapolation that relies on a small portion of channel parameters to infer the remaining channel parameters. Since the substance of channel extrapolation is a mapping from one parameter subspace to another, we can resort to deep learning (DL), a powerful learning architecture, to approximate such mapping function. Specifically, we first analyze the requirements, conditions and challenges for channel extrapolation.Then, we present three typical extrapolations over the antenna dimension, the frequency dimension, and the physical terminal, respectively. We also illustrate their respective principles, design challenges and DL strategies. It will be seen that channel extrapolation could greatly reduce the transmission overhead and subsequently enhance the performance gains compared with the traditional strategies. In S. Zhang and Y. Liu are with the

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“…In the scheme, the inherent correlations in MIMO-OFDM were utilized to improve the performance of estimation. In [14], a channel estimation network and a channel-conditioned recovery network were proposed for channel estimation and signal detection to make them robust to the variation of parameters. However all these works have considered only a single user.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Signal Processing For Multiuser OFDM Systems

Chen

2021

Preprint

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Deep learning (DL) methods have been proved effective in improving the performance of channel estimation and signal detection. In this work, we propose three DL algorithms: fully connected deep neural network (FCDNN), convolutional neural networks (CNN), and long short-term memory (LSTM) neural networks for signal processing in multiuser orthogonal frequency-division multiplexing (OFDM) communications systems. The bit error rates (BERs) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio (SIR), signal-to-noise ratio (SNR), the number of interfering users (NoI) and modulation type are investigated. Numerical results show that all DL methods outperform LMMSE under different multiuser interference conditions, and FCDNN and LSTM give the best and robust anti-multiuser performance. This work shows that FCDNN and LSTM network have strong anti-interference ability and are useful in multiuser OFDM systems.

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Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

Cited by 68 publications

References 14 publications

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions

Deep Learning Based Signal Processing For Multiuser OFDM Systems

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