Non-uniform directional dictionary-based limited feedback for massive MIMO systems

2017 ◽  
Author(s):  
Panos N. Alevizos ◽  
Xiao Fu ◽  
Nicholas Sidiropoulos ◽  
Ye Yang ◽  
Aggelos Bletsas
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Limited Feedback

Fully Convolutional Neural Network Based CSI Limited Feedback for FDD Massive MIMO Systems

2021 ◽  
pp. 1-1
Author(s):  
Guanghui Fan ◽  
Jinlong Sun ◽  
Bamidele Adebisi ◽  
Tomoaki Ohtsuki ◽  
Guan Gui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Limited Feedback

scholarly journals Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1061 ◽  
Author(s):  
Hedi Khammari ◽  
Irfan Ahmed ◽  
Ghulam Bhatti ◽  
Masoud Alajmi
Keyword(s):  
Massive Mimo ◽  
Fourier Transforms ◽  
Mimo Systems ◽  
Limited Feedback ◽  
Base Station ◽  
Discrete Fourier Transforms ◽  
Beam Selection ◽  
Sum Rate ◽  
Hybrid Beamforming ◽  
Csi Feedback

In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27 . 6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62 . 5 % compared to the full CSI feedback.

Deep Learning based CSI Reconstruction with Limited Feedback for Massive MIMO Systems

2021 ◽  
Author(s):  
Xin Wang ◽  
Xiaolin Hou ◽  
Lan Chen ◽  
Yoshihisa Kishiyama ◽  
Takahiro Asai
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Limited Feedback
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