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Deep Learning-based Limited Feedback Designs for MIMO Systems [article]

Jeonghyeon Jang, Hoon Lee, Sangwon Hwang, Haibao Ren, Inkyu Lee
2019 arXiv   pre-print
We study a deep learning (DL) based limited feedback methods for multi-antenna systems.  ...  Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection.  ...  Recently, a deep learning (DL) method has been applied for tackling limited feedback systems design problems [9] - [12] .  ... 
arXiv:1912.09043v1 fatcat:ymbqvincqbbqfhkjsnmdxk37de

Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback [article]

Yifan Ma, Xianghao Yu, Jun Zhang, S.H. Song, Khaled B. Letaief
2022 arXiv   pre-print
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink  ...  Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.  ...  There have been many works that investigated beamforming design for MIMO systems with limited feedback [4] - [6] .  ... 
arXiv:2209.01436v1 fatcat:kfydgtd2xnhs5exxsa2z2ytrbq

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Muddasar Naeem, Giuseppe De Pietro, Antonio Coronato
2021 Sensors  
This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas.  ...  Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback  ...  A blind channel estimation scheme based on DL technology is designed in [153] for OFDM-based large-scale MIMO systems.  ... 
doi:10.3390/s22010309 pmid:35009848 pmcid:PMC8749942 fatcat:2w4th63dtrdyboa6rmhr5rcvja

Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems [article]

Zhenyu Liu, Lin Zhang, Zhi Ding
2019 arXiv   pre-print
This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular  ...  We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.  ...  In [11] , a DL-based channel estimation method was proposed for multi-cell interference-limited massive MIMO systems.  ... 
arXiv:1912.10573v1 fatcat:w2dn4os3yngjjkrzx66utzacga

Scanning the Literature

2021 IEEE wireless communications  
The core of the data processing is a deep-learning based multivariate long short term memory model that captures and predicts the spatiotemporal patterns and mobility.  ...  C-RAN separates a traditional base station into a remote radio head (RRH) and a baseband unit (BBU). The RRH is responsible for radio communication with mobile devices.  ...  Downlink CSI Feedback Algorithm with Deep Transfer Learning for FDD Massive MIMO Systems Jun Zeng, Jinlong Sun, Guan Gui, Bamidele Adebisi, Tomoaki Ohtsuki, Haris Gacanin, and Hikmet Sari, IEEE Transactions  ... 
doi:10.1109/mwc.2021.9535469 fatcat:wno2m5nibbbshb7o2mlmkxeutm

Client Side Channel State Information Estimation for MIMO Communication

Sambhavi Tiwari, Abhishek Abhishek, Shkehar Verma, K Singh, M Syafrullah, Krisna Adiyarta
2019 Proceeding of the Electrical Engineering Computer Science and Informatics  
We introduce a deep reinforcement learning based channel estimation at receiver end for single user MIMO communication without CSI feedback.  ...  However, sending CSI feedback at each time stamp for long duration is an overhead in the communication system.  ...  /limited CSI feedback, it exhibits link adaptation based on short-term feedbacks.  ... 
doi:10.11591/eecsi.v6i0.1993 fatcat:qw5figeg2be7hfd5tezgt6pxem

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Huaming Wu, Xiangyi Li, Yingjun Deng
2020 Journal of Cloud Computing: Advances, Systems and Applications  
As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system models.  ...  Although deep learning has performed well in some IoT applications, "no free lunch" theorem [9] shows that a model cannot solve all problems once and for all, and we cannot learn a general model for a  ...  Acknowledgements The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.  ... 
doi:10.1186/s13677-020-00168-9 fatcat:7n6r2pozgfb5rgfwyxoxpqxq3q

A General Design Framework for MIMO Wireless Energy Transfer With Limited Feedback

Jie Xu, Rui Zhang
2016 IEEE Transactions on Signal Processing  
To overcome this difficulty, under a point-to-point MIMO WET setup, this paper proposes a general design framework for a new type of channel learning method based on the ER's energy measurement and feedback  ...  Based on the energy-level feedback, the ET adjusts transmit beamforming in subsequent training intervals and obtains refined estimates of the MIMO channel by leveraging the technique of analytic center  ...  . • First, we present a general design framework for the energy measurement and feedback based channel learning with limited feedback.  ... 
doi:10.1109/tsp.2016.2526965 fatcat:7jqxgrrv7jhvvbsnoq2z53ku3e

Deep Clustering-Based Codebook Design for Massive MIMO Systems

Jing Jiang, Xiaojing Wang, Wen-Jing Wang, Li Zhen, Junxuan Wang
2019 IEEE Access  
To address this issue, we propose a deep clustering (DC) based codebook design for massive MIMO systems.  ...  INDEX TERMS Massive MIMO, limited feedback, deep-clustering, codebook design.  ...  Therefore, we propose a novel DC based codebook design for massive MIMO systems.  ... 
doi:10.1109/access.2019.2956290 fatcat:gfaeaq2y2bcsfcckfxn7yh24xe

Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO [article]

Foad Sohrabi, Kareem M. Attiah, Wei Yu
2021 arXiv   pre-print
multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS.  ...  This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive  ...  This assumption enables us to investigate the ultimate performance of the deep learning-based precoding design for FDD systems with limited feedback.  ... 
arXiv:2007.06512v2 fatcat:gtnq2adr4jeb5hg4prshc76ogq

Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G [article]

Xin Wang and Xiaolin Hou and Lan Chen and Yoshihisa Kishiyama and Takahiro Asai
2022 arXiv   pre-print
Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry.  ...  Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and  ...  Fig. 2 2 Fig. 2 An example mapping pattern of NR CSI-RS. 3. 2 2 Deep Learning based CSI Schemes 3.2.1 DL-based CSI Reconstruction at Receiver (AI4CSI Rx) Fig. 3 3 Fig. 3 System structure for CSI reconstruction  ... 
arXiv:2206.04967v2 fatcat:jdvf74d3fzanroiziljapmakyy

Deep learning‐based massive multiple‐input multiple‐output channel state information feedback with data normalisation using clipping

Sanguk Jo, Jaehee Lee, Jaewoo So
2021 Electronics Letters  
In this letter, an efficient data normalisation method for deep learning-based CSI feedback in a massive MIMO system is proposed, where the proposed method uses a clipping technique based on the received  ...  Deep learning-based approaches have been proposed to reduce CSI feedback overhead with significant CSI accuracy.  ...  Most previous studies for CSI feedback using deep learning focused on the deep learning architectures.  ... 
doi:10.1049/ell2.12080 fatcat:6masqaa4ovcf3gr6qcxfjuig7y

Table of contents

2021 IEEE Transactions on Wireless Communications  
Norisato Suga and Toshihiro Furukawa Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback .......................................... ................................................  ...  Qiwei Wang and Guangliang Ren Deep Learning for Radio Resource Allocation With Diverse Quality-of-Service Requirements in 5G .................. .....................................  ... 
doi:10.1109/twc.2021.3060977 fatcat:74lib7napnalhfjzhzxlg35ryu

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
., Machine Learning-Enabled LOS/NLOS Identification for MIMO Systems in Dynamic Environments; TWC June 2020 3643-3657 Huang, C., see Yang, M., TWC Sept. 2020 5860-5874 Huang, D., Tao, X., Jiang, C.,  ...  ., +, TWC May 2020 3505-3516 Reconsidering Design of Multi-Antenna NOMA Systems With Limited Feedback.  ...  ., +, TWC Feb. 2020 995-1007 Reconsidering Design of Multi-Antenna NOMA Systems With Limited Feedback.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Samir Salem Al-Bawri, Mohammad Tariqul Islam, Mandeep Jit Singh, Mohd Faizal Jamlos, Adam Narbudowicz, Max J. Ammann, Dominique M. M. P. Schreurs
2022 Computers Materials & Continua  
In addition, a framework has been created for traditional feedback control systems to make RL implementation easier for control systems.  ...  Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system.  ...  Acknowledgement: The authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project number (RSP-2021/387), King Saud University, Riyadh, Saudi Arabia  ... 
doi:10.32604/cmc.2022.021917 fatcat:db33p2eadrgxpdq52db6l6eh5e
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