Deep Learning for Massive MIMO CSI Feedback

@article{Wen2018DeepLF,
  title={Deep Learning for Massive MIMO CSI Feedback},
  author={Chao-Kai Wen and Wan-Ting Shih and Shi Jin},
  journal={IEEE Wireless Communications Letters},
  year={2018},
  volume={7},
  pages={748-751}
}
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 multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a… 
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References

SHOWING 1-10 OF 19 REFERENCES
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems
  • Xiongbin Rao, V. Lau
  • Computer Science, Mathematics
    IEEE Transactions on Signal Processing
  • 2014
TLDR
This paper considers multi-user massive MIMO systems and proposes a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly.
Deep Learning Based MIMO Communications
TLDR
A novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder is introduced and demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems.
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.
Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory
TLDR
Practical open-loop and closed-loop training frameworks are proposed that offer better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.
On Capacity of Large-Scale MIMO Multiple Access Channels with Distributed Sets of Correlated Antennas
In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input
Deep learning for wireless physical layer: Opportunities and challenges
TLDR
This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system and replace the communication system with a radically new architecture based on an autoencoder.
Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas
  • T. Marzetta
  • Computer Science
    IEEE Transactions on Wireless Communications
  • 2010
TLDR
A cellular base station serves a multiplicity of single-antenna terminals over the same time-frequency interval and a complete multi-cellular analysis yields a number of mathematically exact conclusions and points to a desirable direction towards which cellular wireless could evolve.
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.
Message-passing algorithms for compressed sensing
TLDR
A simple costless modification to iterative thresholding is introduced making the sparsity–undersampling tradeoff of the new algorithms equivalent to that of the corresponding convex optimization procedures, inspired by belief propagation in graphical models.
DeepCodec: Adaptive sensing and recovery via deep convolutional neural networks
TLDR
A novel computational sensing framework for sensing and recovering structured signals that learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals.
...
1
2
...