Downlink channel estimation for FDD massive MIMO using conditional generative adversarial networks

B Banerjee, RC Elliott, WA Krzymień… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
For implementation of massive multiple-input multiple-output (MIMO) cellular systems in
frequency division duplex (FDD) mode, accurate estimation of downlink channel state …

Deep learning for massive MIMO CSI feedback

CK Wen, WT Shih, S Jin - IEEE Wireless Communications …, 2018 - ieeexplore.ieee.org
In frequency division duplex mode, the downlink channel state information (CSI) should be
sent to the base station through feedback links so that the potential gains of a massive …

Super-resolution channel estimation for mmWave massive MIMO with hybrid precoding

C Hu, L Dai, T Mir, Z Gao, J Fang - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Channel estimation is challenging for millimeter-wave massive MIMO with hybrid precoding,
since the number of radio frequency chains is much smaller than that of antennas …

Optimized compressive sensing-based direction-of-arrival estimation in massive MIMO

Y Gu, YD Zhang, NA Goodman - 2017 IEEE International …, 2017 - ieeexplore.ieee.org
As a new emerging technology for wireless communications, massive multiple-input multiple-
output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end …

Tensor decomposition-based channel estimation for hybrid mmWave massive MIMO in high-mobility scenarios

R Zhang, L Cheng, S Wang, Y Lou… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave)
can provide unprecedented performance improvement for realizing future wireless …

Deep learning-based downlink channel prediction for FDD massive MIMO system

Y Yang, F Gao, GY Li, M Jian - IEEE Communications Letters, 2019 - ieeexplore.ieee.org
In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO)
system, the acquisition of downlink channel state information (CSI) at base station (BS) is a …

Online deep neural networks for mmWave massive MIMO channel estimation with arbitrary array geometry

X Zheng, VKN Lau - IEEE Transactions on Signal Processing, 2021 - ieeexplore.ieee.org
In this paper, we propose an online training framework for mmWave Massive MIMO channel
estimation (CE) with limited pilots, where the training is based on real-time received pilot …

Estimating doubly-selective channels for hybrid mmWave massive MIMO systems: A doubly-sparse approach

S Gao, X Cheng, L Yang - IEEE Transactions on Wireless …, 2020 - ieeexplore.ieee.org
In mmWave massive multiple-input multiple-output (mMIMO) systems, hybrid (digital/analog)
structure has been a prevalent option to balance system cost and performance. To facilitate …

Sparse Bayesian learning for the time-varying massive MIMO channels: Acquisition and tracking

J Ma, S Zhang, H Li, F Gao, S Jin - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The low-rank property of the channel covariances can be adopted to reduce the overhead of
the channel training in massive MIMO systems. In this paper, with the help of the virtual …

Deep learning-based CSI feedback approach for time-varying massive MIMO channels

T Wang, CK Wen, S Jin, GY Li - IEEE Wireless …, 2018 - ieeexplore.ieee.org
Massive multiple-input multiple-output (MIMO) systems rely on channel state information
(CSI) feedback to perform precoding and achieve performance gain in frequency division …