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Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems

Junyong Shin, Yujin Kang, Yo-Seb Jeon

TL;DR

Simulation results demonstrate that the proposed method reduces the computational complexity of VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.

Abstract

This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity associated with VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.

Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems

TL;DR

Simulation results demonstrate that the proposed method reduces the computational complexity of VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.

Abstract

This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity associated with VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.
Paper Structure (11 sections, 7 equations, 3 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 7 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Preliminary
  3. FDD Massive MIMO System
  4. Vector-Quantized Variational Autoencoder (VQ-VAE)
  5. Proposed DL-based CSI Feedback Method
  6. Basic Idea: Shape-Gain Quantization
  7. Gain (Magnitude) Quantization
  8. Shape (Direction) Quantization
  9. Multi-Rate Codebook Design
  10. Simulation Results
  11. Conclusion

Figures (3)

  • Figure 1: An illustration of the proposed DL-based CSI feedback method using shape-gain vector quantization.
  • Figure 2: Comparison of the NMSE performance of the proposed method with and without the multi-rate codebook design when $L=4$.
  • Figure 3: Comparison of the performance-complexity trade-off achieved by the proposed and original VQ-VAE methods.