Deep Residual Learning Meets OFDM Channel Estimation

Li, Lianjun; Chen, Hao; Chang, Hao-Hsuan; Liu, Lingjia

doi:10.1109/lwc.2019.2962796

Cited by 113 publications

(52 citation statements)

References 6 publications

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“…To trakle the time-varying Rayleigh fading channel, a sliding bidirectional gated recurrent unit channel estimator was designed to improve the channel estimation performance [25]. In [26], a residual learning based deep neural network (DNN) was designed for channel estimation. Although DNNs were used to implement a joint scheme of pilot training and channel estimation, the number of linear layers in DNN was sensitive to neurons size and the length of input data.…”

Section: A Recent Workmentioning

confidence: 99%

Offset Learning Based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication

Chen

Tang

Zhang

et al. 2022

IEEE J. Sel. Top. Signal Process.

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The emerging intelligent reflecting surface (IRS) can significantly improve the system capacity, and it has been regarded as a promising technology for the beyond fifth-generation (B5G) communications. For IRS-assisted multiple input multiple output (MIMO) systems, accurate channel estimation is a critical challenge. This severely restricts practical applications, particularly for resource-limited indoor scenario as it contains numerous scatterers and parameters to be estimated, while the number of pilots is limited. Prior art tackles these issues and associated optimization using mathematical-based statistical approaches, but are difficult to solve as the number of scatterers increase. To estimate the indoor channels with an affordable piloting overhead, we propose an offset learning (OL)-based neural network for channel estimation. The proposed OL-based estimator can dynamically trace the channel state information (CSI) without any prior knowledge of the IRS-assisted channel structure as well as indoor statistics. In addition, inspired by the powerful learning capability of convolutional neural network (CNN), CNN-based inversion blocks are developed in the offset estimation module to build the offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor

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Section: A Recent Workmentioning

confidence: 99%

Offset Learning Based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication

Chen

Tang

Zhang

et al. 2022

IEEE J. Sel. Top. Signal Process.

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“…However, the assumption that pilot length is larger than the antennas at the BS in the mm Wave massive MIMO system makes channel estimation computationally complicated and creates a huge pilot overhead. In recent years, deep learning (DL) has attracted the attention of researchers in wireless communication fields and has been successfully applied to key physical layer techniques such as modulation pattern recognition [ 7 , 8 , 9 , 10 ], blind channel equalization [ 11 ], channel decoding [ 12 , 13 ] and channel estimation [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. The authors of [ 14 ] use powerful deep learning to address the orthogonal frequency division multiplexing (OFDM) system in an End-to-End manner for combating nonlinear distortion and interference.…”

Section: Introductionmentioning

confidence: 99%

“…A deep denoising convolutional neural network (DnCNN) for improving the model’s robustness is proposed in [ 16 ], which learns rapidly changing channel characteristics and accurately estimates the channel amplitudes for frequency-selective channel estimation. Motivated by the advantages of residual learning, the studies in [ 17 , 18 , 19 , 20 ] introduce a residual learning based estimator, which greatly reduces the implementation complexity. The loss functions for channel estimation are not well designed in a mm Wave massive MIMO system.…”

Section: Introductionmentioning

confidence: 99%

Residual Learning and Multi-Path Feature Fusion-Based Channel Estimation for Millimeter-Wave Massive MIMO System

Zheng

Liu

Liang

et al. 2022

Entropy

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Channel estimation is a challenging task in a millimeter-wave (mm Wave) massive multiple-input multiple-output (MIMO) system. The existing deep learning scheme, which learns the mapping from the input to the target channel, has great difficulty in estimating the exact channel state information (CSI). In this paper, we consider the quantized received measurements as a low-resolution image, and we adopt the deep learning-based image super-resolution technique to reconstruct the mm Wave channel. Specifically, we exploit a state-of-the-art channel estimation framework based on residual learning and multi-path feature fusion (RL-MFF-Net). Firstly, residual learning makes the channel estimator focus on learning high-frequency residual information between the quantized received measurements and the mm Wave channel, while abundant low-frequency information is bypassed through skip connections. Moreover, to address the estimator’s gradient dispersion problem, a dense connection is added to the residual blocks to ensure the maximum information flow between the layers. Furthermore, the underlying mm Wave channel local features extracted from different residual blocks are preserved by multi-path feature fusion. The simulation results demonstrate that the proposed scheme outperforms traditional methods as well as existing deep learning methods, especially in the low signal-to-noise-ration (SNR) region.

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“…The authors of [15] propose a datadriven end-to-end DNN-based CSI compression feedback and recovery mechanism which is further extended with long shortterm memory (LSTM) to tackle time-varying massive MIMO channels [16]. To achieve better estimation performance and reduce computation cost, a compact and flexible deep residual network architecture is proposed to conduct channel estimation for an OFDM system based on downlink pilots in [17]. Nevertheless, the performance of the data-driven approaches heavily depends on an enormous amount of labeled data which cannot be easily obtained in wireless communication.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Analysis of Deep Neural Networks in Physical Layer Communication

Li¹,

Zhao²,

Ma³

et al. 2022

Preprint

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Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the "black box" of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.

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Deep Residual Learning Meets OFDM Channel Estimation

Cited by 113 publications

References 6 publications

Offset Learning Based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication

Offset Learning Based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication

Residual Learning and Multi-Path Feature Fusion-Based Channel Estimation for Millimeter-Wave Massive MIMO System

Theoretical Analysis of Deep Neural Networks in Physical Layer Communication

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