Simultaneous Learning and Inferencing of DNN-Based mmWave Massive MIMO Channel Estimation in IoT Systems With Unknown Nonlinear Distortion

Zheng, Xuanyu; Lau, Vincent K. N.

doi:10.1109/jiot.2021.3085659

Cited by 10 publications

(2 citation statements)

References 33 publications

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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%

“…Specifically, the entire neural network is divided into the recurrent neural network (RNN) and the enhanced feedforward neural network (FNN) to achieve better extrapolation performance. In [ 25 ], a two-stage DNN structure with nonlinear modules is proposed to simultaneously generate channel estimation in real-time. The simulation shows that the proposed method is robust to all kinds of nonlinear channel distortion.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Liu

Liang

et al. 2022

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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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