# BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction

arXiv: https://arxiv.org/abs/2501.01802 · HTML: https://mlsanity.com/paper/2501.01802/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026)
- [AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G](https://mlsanity.com/paper/2605.00020/) (2026)
- [Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models](https://mlsanity.com/paper/2605.00968/) (2026)
- [Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels](https://mlsanity.com/paper/2505.13055/) (2025)
- [A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff](https://mlsanity.com/paper/2507.18587/) (2025)
- [A Short Overview of Multi-Modal Wi-Fi Sensing](https://mlsanity.com/paper/2505.06682/) (2025)
- [SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction](https://mlsanity.com/paper/2606.00120/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)

## Abstract (arXiv)

Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communication system in terms of capacity, spectral, and energy efficiency. The performance of MIMO systems is highly dependent on the quality of channel state information (CSI). Predicting CSI is, therefore, essential for improving communication system performance, particularly in MIMO systems, since it represents key characteristics of a wireless channel, including propagation, fading, scattering, and path loss. This study proposes a foundation model inspired by BERT, called BERT4MIMO, which is specifically designed to process high-dimensional CSI data from massive MIMO systems. BERT4MIMO offers superior performance in reconstructing CSI under varying mobility scenarios and channel conditions through deep learning and attention mechanisms. The experimental results demonstrate the effectiveness of BERT4MIMO in a variety of wireless environments.