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

TL;DR

This paper introduces a hybrid approach combining neural networks with traditional state-space models for accurate, interpretable, and label-free prediction of mmWave channels, addressing limitations of existing methods.

Contribution

It presents a novel hybrid method integrating neural networks into a model-based framework with an unsupervised training strategy, enhancing prediction accuracy and interpretability without labeled data.

Findings

Superior prediction accuracy over state-of-the-art methods

Robustness against high noise levels and severe channel variations

Effective tracking of complex channel dynamics without expert knowledge

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.