Reciprocity Aided CSI Feedback for Massive MIMO

Ema Becirovic ◽  
Emil Bjornson ◽  
Erik G. Larsson
Wenbo Zeng ◽  
Yigang He ◽  
Bing Li ◽  
Shudong Wang

Yuting Wang ◽  
Yibin Zhang ◽  
Jinlong Sun ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  

2021 ◽  
pp. 1-1
Zhengyang Hu ◽  
Jianhua Guo ◽  
Guanzhang Liu ◽  
Hanying Zheng ◽  
Jiang Xue

2020 ◽  
Vol 24 (12) ◽  
pp. 2805-2808
Bassant Tolba ◽  
Maha Elsabrouty ◽  
Mubarak G. Abdu-Aguye ◽  
Haris Gacanin ◽  
Hossam Mohamed Kasem

2020 ◽  
Vol 24 (10) ◽  
pp. 2319-2323
Yaqiong Zhao ◽  
Wei Xu ◽  
Jindan Xu ◽  
Shi Jin ◽  
Kezhi Wang ◽  

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1061 ◽  
Hedi Khammari ◽  
Irfan Ahmed ◽  
Ghulam Bhatti ◽  
Masoud Alajmi

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.

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