# Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning

arXiv: https://arxiv.org/abs/2009.09967 · HTML: https://mlsanity.com/paper/2009.09967/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 21 of 21)

- [Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning](https://mlsanity.com/paper/2409.00124/) (2024)
- [A Comparison of Neural Networks for Wireless Channel Prediction](https://mlsanity.com/paper/2308.14020/) (2023)
- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments](https://mlsanity.com/paper/2206.02165/) (2022)
- [Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training](https://mlsanity.com/paper/2409.00005/) (2024)
- [LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction](https://mlsanity.com/paper/2410.21351/) (2024)
- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation](https://mlsanity.com/paper/2110.00414/) (2021)
- [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Decision Feedback In-Context Symbol Detection over Block-Fading Channels](https://mlsanity.com/paper/2411.07600/) (2024)
- [Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction](https://mlsanity.com/paper/2411.11576/) (2024)
- [Reverse Ordering Techniques for Attention-Based Channel Prediction](https://mlsanity.com/paper/2302.00341/) (2023)
- [Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction](https://mlsanity.com/paper/2409.09978/) (2024)
- [Large Models Enabled Ubiquitous Wireless Sensing](https://mlsanity.com/paper/2411.18277/) (2024)
- [Decision Feedback In-Context Learning for Wireless Symbol Detection](https://mlsanity.com/paper/2503.16594/) (2025)
- [SCA-LLM: Spectral-Attentive LLM-Based Wireless World Modeling for Agentic Communications](https://mlsanity.com/paper/2509.08139/) (2025)
- [CSI Prediction Using Diffusion Models](https://mlsanity.com/paper/2510.11214/) (2025)
- [CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing](https://mlsanity.com/paper/2510.12996/) (2025)
- [ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture](https://mlsanity.com/paper/2605.12553/) (2026)
- [TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction](https://mlsanity.com/paper/2608.27124/) (2026)

## Abstract (arXiv)

This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor.