Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Generative vs. Predictive Models in Massive MIMO Channel Prediction

Ju-Hyung Lee, Joohan Lee, Andreas F. Molisch

TL;DR

This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction and compares Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments.

Abstract

Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard AEs struggle under noisy channel conditions, limiting their effectiveness. This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction. We compare Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments. The proposed VQ-VAE achieves up to 15 [dB] NMSE gains over standard AEs and about 9 [dB] over VAEs. Additionally, we present a complexity analysis of AE-based models alongside a diffusion model, highlighting the trade-off between accuracy and computational efficiency.

Generative vs. Predictive Models in Massive MIMO Channel Prediction

TL;DR

This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction and compares Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments.

Abstract

Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard AEs struggle under noisy channel conditions, limiting their effectiveness. This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction. We compare Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments. The proposed VQ-VAE achieves up to 15 [dB] NMSE gains over standard AEs and about 9 [dB] over VAEs. Additionally, we present a complexity analysis of AE-based models alongside a diffusion model, highlighting the trade-off between accuracy and computational efficiency.

Paper Structure

This paper contains 10 sections, 5 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. VQ-VAE for mMIMO Channel Prediction
  4. Task: Cross-Antenna Channel Prediction
  5. Approach: Vector Quantized VAE (VQ-VAE)
  6. Evaluation & Training
  7. Experiments
  8. Simulation Setup
  9. Simulation Results
  10. Conclusions

Figures (5)

  • Figure 1: AE vs. VAE model.
  • Figure 2: VQ-VAE based mMIMO channel prediction.
  • Figure 3: Prediction results from AE, VAE, and VQ-VAE under good ($\gamma=30$ [dB]) and noisy ($\gamma=0$ [dB]) channel conditions, compared with the ground truth (Latent dim. = 64).
  • Figure 4: Comparative Analysis: NMSE vs. SNR [dB] for AE, VAE, and VQ-VAE.
  • Figure 5: Prediction results of the proposed VQ-VAE on Out-Of-Distribution (OOD) channels ($\gamma=30$ [dB], Latent dim. = 64). The top row shows the ground-truth, while the bottom row presents the corresponding predictions.