A Partial Reciprocity-based Channel Prediction Framework for FDD massive MIMO with High Mobility

@article{Qin2022APR,
  title={A Partial Reciprocity-based Channel Prediction Framework for FDD massive MIMO with High Mobility},
  author={Ziao Qin and Haifan Yin and Yandi Cao and Weidong Li and David Gesbert},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.05564}
}
—Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 46 REFERENCES
Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise
TLDR
It is found through perturbation analysis and simulation that, for signals with unknown damping factors, the pencil method is less sensitive to noise than the polynomial method.
Addressing the Curse of Mobility in Massive MIMO With Prony-Based Angular-Delay Domain Channel Predictions
TLDR
A novel form of channel prediction method, named Prony-based angular-delay domain (PAD) prediction, which is built on exploiting the specific angle-delay-Doppler structure of the multipath, which relies on the high angular- delay resolution which arises in the context of 5G.
Deep Learning-Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction
TLDR
Seeing the channel as an image, You Only Look Once (YOLO), a powerful neural network for object detection, is introduced to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers.
An efficient augmented Lagrangian method with applications to total variation minimization
TLDR
An algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but not necessarily convex programs) with a particular structure that effectively combines an alternating direction technique with a nonmonotone line search to minimize the augmented Lagrangian function at each iteration is proposed.
A Partial Channel Reciprocity-based Codebook for Wideband FDD Massive MIMO
TLDR
Simulations with the practical 3GPP channel model show the significant gains over the latest 5G codebook, which prove that the proposed methods are practical solutions for 5G and beyond.
Channel Feedback in TDD Massive MIMO Systems With Partial Reciprocity
Channel Prediction in High-Mobility Massive MIMO: From Spatio-Temporal Autoregression to Deep Learning
TLDR
Simulation results under the 3GPP non-line- of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy.
Downlink Channel Reconstruction for Spatial Multiplexing in Massive MIMO Systems
TLDR
Numerical results show that the spectral efficiencies by spatial multiplexing based on the proposed downlink MIMO CSI reconstruction techniques outperform the conventional methods solelybased on the quantized CSI.
Massive MIMO Channel Prediction: Kalman Filtering Vs. Machine Learning
TLDR
This paper develops and compares a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML) based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years.
Joint Spatial Division and Multiplexing in Massive MIMO: A Neighbor-Based Approach
TLDR
An optimal prebeamformer which is proved to be able to achieve the same system capacity as the full CSI system is proposed, followed by a suboptimal pre beamformer with constrained DTL, and a DFT-based prebeamforming.
...
...