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PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems.

Xiuwen Bao1, Ming Jiang1,2, Wenhao Fang1

  • 1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China.

Entropy (Basel, Switzerland)
|August 26, 2022
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Summary

This study introduces PCQNet, a CNN-based network to reduce feedback overhead in uplink MU-MIMO systems. PCQNet achieves near-optimal performance with significantly less feedback, improving spectral and energy efficiency.

Keywords:
MIMOMMSE receiversconvolutional neural networks (CNNs)joint transceiver designlimited feedback precodinguplink precoding

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Area of Science:

  • Wireless communication networks
  • Signal processing
  • Machine learning for wireless systems

Background:

  • Multi-user multiple-input multiple-output (MU-MIMO) enhances spectral and energy efficiency in wireless networks.
  • Uplink MU-MIMO systems require precoding matrix feedback from base stations to user equipment for optimal performance.
  • Minimizing the mean squared error (MSE) under power constraints is crucial for uplink MU-MIMO precoder design.

Purpose of the Study:

  • To propose a novel Convolutional Neural Network (CNN)-based compression network, PCQNet, for minimizing feedback overhead in uplink MU-MIMO systems.
  • To investigate the impact of trainable compression ratios and feedback bits on the accuracy of recovered precoding matrices.
  • To evaluate the performance of PCQNet in a centralized system using block error rates and minimum mean-squared error (MMSE) transceivers.

Main Methods:

  • Development of PCQNet, a CNN-based architecture for compressing precoding matrices.
  • Analysis of Mean Squared Error (MSE) between original and recovered precoding matrices with varying compression parameters.
  • Performance evaluation using block error rates (BLER) in a centralized MMSE transceiver system.

Main Results:

  • PCQNet effectively minimizes feedback overhead by compressing precoding matrices.
  • Trainable compression ratios and feedback bits demonstrate a clear trade-off with MSE.
  • The proposed PCQNet achieves near-optimal performance compared to existing quantized feedback schemes.
  • Significant reduction in feedback overhead with negligible performance degradation was observed.

Conclusions:

  • PCQNet offers an efficient solution for reducing feedback overhead in uplink MU-MIMO systems.
  • The CNN-based approach provides a practical method for joint transceiver design with reduced communication costs.
  • PCQNet enables substantial improvements in wireless network efficiency without compromising performance.