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2021 IEEE Communications Letters  
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

K. Satyanarayana, Mohammed El-Hajjar, Alain Mourad, Lajos Hanzo
2019 IEEE Access  
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

Amanda Ly, Yu-Dong Yao
2021 IEEE Open Journal of the Communications Society  
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

Sai Huang, Meng Zhang, Yicheng Gao, Zhiyong Feng
2021 IEEE Transactions on Wireless Communications  
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

Qiujin Lu, Tian Lin, Yu Zhu
2021 IEEE Access  
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]

Peihao Dong, Hua Zhang, Geoffrey Ye Li
2020 arXiv   pre-print
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

Peihao Dong, Hua Zhang, Geoffrey Ye Li
2020 IEEE Access  
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]

Shicong Liu, Zhen Gao, Jun Zhang, Marco Di Renzo, Mohamed-Slim Alouini
2020 arXiv   pre-print
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

ShuFen Liang, Huilin Liu, Chen Chen, Chuanbo Qin, FangChen Yang, Yue Feng, Zhuosheng Lin, Gulistan Raja
2021 PLoS ONE  
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]

Asmaa Abdallah, Abdulkadir Celik, Mohammad M. Mansour, Ahmed M. Eltawil
2021 arXiv   pre-print
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]

Fuxin Zhang, Chunbo Luo, Jialang Xu, Yang Luo, FuChun Zheng
2022 arXiv   pre-print
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  
., +, 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  
., 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]

Eren Balevi, Akash Doshi, Ajil Jalal, Alexandros Dimakis, Jeffrey G. Andrews
2020 arXiv   pre-print
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

Ravilla Dilli
2021 Wireless networks  
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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