Multi-resolution CSI Feedback with Deep Learning in Massive MIMO System

@article{Lu2020MultiresolutionCF,
  title={Multi-resolution CSI Feedback with Deep Learning in Massive MIMO System},
  author={Zhilin Lu and Jintao Wang and Jian Song},
  journal={ICC 2020 - 2020 IEEE International Conference on Communications (ICC)},
  year={2020},
  pages={1-6}
}
  • Zhilin Lu, Jintao Wang, Jian Song
  • Published 31 October 2019
  • Computer Science, Engineering, Mathematics
  • ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple… 

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References

SHOWING 1-10 OF 18 REFERENCES
Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels
TLDR
A real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), is developed by extending a novel deep learning (DL)-based CSI sensing and recovery network that outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.
Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
TLDR
A multiple-rate compressive sensing neural network framework to compress and quantize the CSI, which not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility.
Deep Learning for Massive MIMO CSI Feedback
TLDR
CsiNet is developed, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples that can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods.
Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback
TLDR
Two deep learning architectures are proposed, Dual net-MAG and DualNet-ABS, to significantly reduce the CSI feedback payload based on the multipath reciprocity, based on limited feedback and bi-directional reciprocal channel characteristics.
Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System
TLDR
Numerical results show that the SCNet achieves better performance than general deep networks in terms of prediction accuracy and exhibits remarkable robustness over complicated wireless channels, demonstrating its great potential for practical deployments.
Compressed channel feedback for correlated massive MIMO systems
TLDR
A new sparsifying basis that reflects the long-term characteristics of the channel and a new reconstruction algorithm for CS is proposed and it is suggested that dimensionality reduction is more proper to compress, and compare performance with the conventional method.
Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays
TLDR
This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load.
Massive MIMO for next generation wireless systems
TLDR
While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
Deep Learning Approach Based on Tensor-Train for Sparse Signal Recovery
TLDR
The proposed TT-SDA network can preserve the reconstruction performance of the conventional SDA network and outperform the traditional methods, especially with low measurement rates, and it can also significantly reduce the computational complexity and occupied memory space, which becomes a time and memory efficient method in compressive sensing problem.
The COST 2100 MIMO channel model
TLDR
An overview of the COST 2100 channel model is presented, including dense multipath components, polarization, and multi-link aspects, which make it suitable to model multi-user or distributed MIMO scenarios.
...
1
2
...