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

arXiv: https://arxiv.org/abs/2411.11576 · HTML: https://mlsanity.com/paper/2411.11576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 68 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [Variational Gaussian Process State-Space Models](https://mlsanity.com/paper/1406.4905/) (2014)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Unsupervised Learned Kalman Filtering](https://mlsanity.com/paper/2110.09005/) (2021)
- [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022)
- [How Much Data is Needed for Channel Knowledge Map Construction?](https://mlsanity.com/paper/2312.06966/) (2023)
- [Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictions](https://mlsanity.com/paper/1912.11330/) (2019)
- [Unsupervised Learning of Sampling Distributions for Particle Filters](https://mlsanity.com/paper/2302.01174/) (2023)
- [AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications](https://mlsanity.com/paper/2106.00171/) (2021)
- [Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach](https://mlsanity.com/paper/1812.00541/) (2018)
- [Delay and reliability-constrained VNF placement on mobile and volatile 5G infrastructure](https://mlsanity.com/paper/2007.11870/) (2020)
- [Channel Tracking and Prediction for IRS-aided Wireless Communications](https://mlsanity.com/paper/2203.05870/) (2022)
- [Performance Analysis of Uplink/Downlink Decoupled Access in Cellular-V2X Networks](https://mlsanity.com/paper/2405.06339/) (2024)

## Built on by (most cited first, 2 of 2)

- [Integrated Image Reconstruction and Target Recognition based on Deep Learning Technique](https://mlsanity.com/paper/2505.04836/) (2025)
- [CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing](https://mlsanity.com/paper/2510.12996/) (2025)

## Abstract (arXiv)

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.