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Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction

Yiyong Sun, Jiajun He, Zhidi Lin, Wenqiang Pu, Feng Yin, Hing Cheung So

TL;DR

Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven.

Abstract

Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations and high noise levels.

Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction

TL;DR

Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven.

Abstract

Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations and high noise levels.

Paper Structure

This paper contains 21 sections, 3 theorems, 33 equations, 13 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions
  4. Time-varying Channel Prediction
  5. Background
  6. Channel Model
  7. Dynamic Condition
  8. Signal Model
  9. Problem Formulation
  10. Kalman Prediction Integrated with Neural Network
  11. Model-based SSM
  12. Problems with Model-based FTP Workflow
  13. KPIN-enabled Hybrid FTP Workflow
  14. Low-complexity Unsupervised Learning
  15. Discussions
  16. ...and 6 more sections

Key Result

Proposition 1

Assuming a linear structure and i.i.d. AWGN in the approximated channel dynamics (eq-ar), we have the following Yule-Walker equations yule1971methodwalker1931periodicity: where the right-hand side (RHS) matrix $\mathbf C$ and the coefficient matrix $\mathbf C _{\text{all}}$ are defined in (eq-C-vec_main) and (eq-C-all-even_main), respectively, based on the auto-covariance matrix $\mathbf{C}_k$ de

Figures (13)

  • Figure 1: The schematic diagram of V2X scenario liao2019ekf.
  • Figure 2: Latent state augmentation with $p=3$.
  • Figure 3: Hybrid FTP workflow consists of multiple KPIN modules with shared parameters $\boldsymbol \psi$, depicted in the orange areas, invoked at each time step. Solid arrows connect data-driven components (\ref{['eq-output-kpin']}). Dashed arrows represent model-based components (\ref{['eq-kpin-posterior']}, \ref{['eq-kpin-input']}, \ref{['eq-kpin-predict']}, \ref{['eq-kpin-extract']}), as further illustrated in Fig. \ref{['fig-train']}. Blue rectangles correspond to the loss terms in (\ref{['eq-ls']}).
  • Figure 4: Information flow through the model-based components.
  • Figure 5: Mean and standard deviation of NSE versus future time step for five methods.
  • ...and 8 more figures

Theorems & Definitions (10)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Proposition 1
  • Proposition 2
  • Remark 5
  • Remark 6
  • Proposition 3
  • Remark 7