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2021
IEEE Communications Letters
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Wong 3898 Two-Stage Hybrid Precoding for Minimizing Residuals Using Convolutional Neural Network ........................... ............................................................................ ...
Kim 3878 Convolutional Neural Network (CNN)-Based Detection for Multi-Level-Cell NAND Flash Memory ................... ...
doi:10.1109/lcomm.2021.3129865
fatcat:526cbdhehvegvarba5zcprdqh4
Multi-User Full Duplex Transceiver Design for mmWave Systems Using Learning-Aided Channel Prediction
2019
IEEE Access
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We first derive a joint precoder and combiner design for full duplex K -user MIMO-OFDM interference channels, where we aim for minimizing both the residual SI and the MI, followed by an iterative hybrid ...
Then, we propose a learningaided channel prediction technique for systems suffering from channel aging relying on a radial basis neural network, where we show by simulation that upon using sufficient training ...
On the other hand, the complexity of the radial basis neural network used for channel prediction involves two phases: offline and online. ...
doi:10.1109/access.2019.2916799
fatcat:4c55qagiirdwtjlrfbjfldw3tq
A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security
2021
IEEE Open Journal of the Communications Society
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However, there is still a demand concerning 5G research for service and performance improvement. ...
This article provides a comprehensive review of 5G communications research using deep learning. ...
Unlike Jin, Huang et al. focused on a mmWave massive MIMO framework for effective hybrid precoding using a DNN. ...
doi:10.1109/ojcoms.2021.3058353
fatcat:vqyfhhm4gnb4po4nhtjch7dlpe
MIMO Radar Aided mmWave Time-varying Channel Estimation in MU-MIMO V2X Communications
2021
IEEE Transactions on Wireless Communications
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Robust channel estimation in time-varying channels is used to guarantee the quality of communication services, especially for Vehicle-to-Everything (V2X) scenarios. ...
In this paper, we propose a MIMO radar aided channel estimation scheme using deep learning (DL) for the uplink mmWave multiuser (MU)-MIMO communications. ...
For image restoration, a feed forward denoising convolution neural network (CNN) called DnCNN is designed in [29] , where residual learning and batch normalization is utilized to improve the denoising ...
doi:10.1109/twc.2021.3085823
fatcat:sy3lsazcdzg3picqoofylioih4
Channel Estimation and Hybrid Precoding for Millimeter Wave Communications: A Deep Learning-based Approach
2021
IEEE Access
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In this paper, we investigate the channel estimation and hybrid precoding for mmWave MIMO systems with deep learning. ...
With the estimated channel state information (CSI) as the input, we develop a robust HBF network (HBF-Net) by applying convolutional layers and attention mechanism, which can be trained to generate a robust ...
The authors in [15] proposed a deep NN (DNN) framework to construct an auto-precoder. The authors in [17] unfolded the gradient ascent beamforming algorithm with a residual neural network. ...
doi:10.1109/access.2021.3108625
fatcat:h6uwrkt4cfcz3mifpc6lqu42zy
Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems
[article]
2020
arXiv
pre-print
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The proposed framework includes three parts: hybrid processing designer, signal flow simulator, and signal demodulator, which outputs the hybrid processing matrices for the transceiver by using neural ...
networks (NNs), simulates the signal transmission over the air, and maps the detected symbols to the original bits by using the NN, respectively. ...
For wideband mmWave massive MIMO systems in timevarying channels, channel correlation has been exploited by deep convolutional neural network (CNN) in [24] to improve the accuracy and accelerate the ...
arXiv:2006.03215v1
fatcat:swbv4dohobaqhniynejzv3mv7a
Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems
2020
IEEE Access
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The proposed framework includes three parts: hybrid processing designer, signal flow simulator, and signal demodulator, which outputs the hybrid processing matrices for the transceiver by using neural ...
networks (NNs), simulates the signal transmission over the air, and maps the detected symbols to the original bits by using the NN, respectively. ...
For wideband mmWave massive MIMO systems in time-varying channels, channel correlation has been exploited by deep convolutional neural network (CNN) in [24] to improve the accuracy and accelerate the ...
doi:10.1109/access.2020.3000601
fatcat:zyyu6bg7ozbfhhkldtsdd4ftou
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
[article]
2020
arXiv
pre-print
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Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. ...
Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. ...
networks can be used to this matrix for improved estimation accuracy as shown in Fig.2 . ...
arXiv:2006.02201v2
fatcat:cxfh45w43jdvdoryqf5s5ygaay
Research on multi-path dense networks for MRI spinal segmentation
2021
PLoS ONE
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Instead of the standard convolution structure, we apply a new type of convolution module for the feature extraction. ...
To address these problems, this study proposes a series of improved models for semantic segmentation and progressively optimizes them from the three aspects of convolution module, codec unit, and feature ...
The networks used different codec paths as the model frameworks for spine image segmentation. The MC and DAB modules were applied to form six hybrid networks. ...
doi:10.1371/journal.pone.0248303
pmid:33711080
fatcat:zna3n4cefvfedgwwizr5nd2vgu
Deep Learning Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems
[article]
2021
arXiv
pre-print
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In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. ...
In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation. ...
., separating the noise from a noisy image by feed-forward convolutional neural networks (CNNs). ...
arXiv:2102.10847v1
fatcat:jlhxzfxzqzetfi6yki44jtwe2m
Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges
[article]
2022
arXiv
pre-print
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networks. ...
in the new multiple-input-multiple-output (MIMO) scenario with precoding. ...
Therefore, researchers have proposed to combine the characteristics of both types of neural network layers to build hybrid models for AMR. ...
arXiv:2207.09647v1
fatcat:mcotsmsnuvb65l7ph5leevlou4
2019 Index IEEE Wireless Communications Letters Vol. 8
2019
IEEE Wireless Communications Letters
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., +, LWC Feb. 2019 57-60 Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems. ...
., +, LWC Feb. 2019 29-32
Deep Convolutional Neural Networks for Link Adaptations in MIMO-
OFDM Wireless Systems. ...
doi:10.1109/lwc.2019.2961756
fatcat:bwxehcl4ejew7a6m66prb6s4z4
2020 Index IEEE Transactions on Wireless Communications Vol. 19
2020
IEEE Transactions on Wireless Communications
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., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua, C., see Li, M ...
TWC Jan. 2020 650-664 Huang, A., see He, H., TWC Dec. 2020 7881-7896 Huang, C., Molisch, A.F., He, R., Wang, R., Tang, P., Ai, B., and Zhong, Z., Machine Learning-Enabled LOS/NLOS Identification for ...
., +, TWC Dec. 2020 7973-7985 Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis. ...
doi:10.1109/twc.2020.3044507
fatcat:ie4rwz4dgvaqbaxf3idysubc54
High Dimensional Channel Estimation Using Deep Generative Networks
[article]
2020
arXiv
pre-print
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Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. ...
received signal and the generator's channel estimate while minimizing the rank of the channel estimate. ...
The authors would like to thank Nitin Myers for discussions on low resolution quantization and Shilpa Talwar, Nageen Himayat, Ariela Zeira at Intel for their invaluable support and technical advice and ...
arXiv:2006.13494v1
fatcat:uoieantwpzf7dojwqe44v4dwbu
Performance analysis of multi user massive MIMO hybrid beamforming systems at millimeter wave frequency bands
2021
Wireless networks
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It emphasizes the hybrid precoding at transmitter and combining at receiver of a mmWave MU-mMIMO hybrid beamforming system. ...
AbstractMillimeter-wave (mmWave) and massive multi-input–multi-output (mMIMO) communications are the most key enabling technologies for next generation wireless networks to have large available spectrum ...
Fig. 1 1 MU-mMIMO Hybrid beamforming system at the transmitter Wireless Networks (2021)
Fig. 3 3 Block diagram of Precoding stages at Receiver in mmWave MU-MIMO hybrid beamforming system
h 11 h 21 ...
doi:10.1007/s11276-021-02546-w
fatcat:t3mditk56jewbjwkmjfuuiiwb4
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