# WirelessJEPA: A Multi-Antenna Foundation Model using Spatio-temporal Wireless Latent Predictions

arXiv: https://arxiv.org/abs/2601.20190 · HTML: https://mlsanity.com/paper/2601.20190/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2026.

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

- [AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling](https://mlsanity.com/paper/2605.05586/) (2026)
- [CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision](https://mlsanity.com/paper/2605.14171/) (2026)
- [WiFi-JEPA: Self-supervised Learning for WiFi-CSI 3D Human Pose Estimation](https://mlsanity.com/paper/2607.11064/) (2026)
- [LatentWave: JEPA Pretraining for Wireless Foundation Models](https://mlsanity.com/paper/2606.06373/) (2026)
- [PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels](https://mlsanity.com/paper/2605.22856/) (2026)
- [SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction](https://mlsanity.com/paper/2606.00120/) (2026)
- [JEPA for AI-Native 6G: Predictive Representations and Open Challenges](https://mlsanity.com/paper/2607.09798/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)
- [Scaling of Wireless Foundation Models via Representation Diversity and Multi-Branch Architectures](https://mlsanity.com/paper/2610.04289/) (2026)

## Abstract (arXiv)

We propose WirelessJEPA, a novel wireless foundation model (WFM) that uses the Joint Embedding Predictive Architecture (JEPA). WirelessJEPA learns general-purpose representations directly from real-world multi-antenna IQ data by predicting latent representations of masked signal regions. This enables multiple diverse downstream tasks without reliance on carefully engineered contrastive augmentations. To adapt JEPA to wireless signals, we introduce a 2D antenna time representation that reshapes multi-antenna IQ streams into structured grids, allowing convolutional processing with block masking and efficient sparse computation over unmasked patches. Building on this representation, we propose novel spatio temporal mask geometries that encode inductive biases across antennas and time. We evaluate WirelessJEPA across six downstream tasks and demonstrate it's robust performance and strong task generalization. Our results establish that JEPA-based learning as a promising direction for building generalizable WFMs.