JEPA-MSAC: A Joint-Embedding Predictive Architecture for Multimodal Sensing-Assisted Communications
Hey there, little explorer! Imagine you have a special toy robot that helps your walkie-talkie talk super well, even when you're running around!
Scientists made a new smart helper called JEPA-MSAC. It's like a super-smart detective for your walkie-talkie!
This detective watches everything around it – like how loud sounds are or where things are hiding. It learns to guess what will happen next, like predicting where your friend will run.
Then, it helps your walkie-talkie send messages faster and clearer! It's like giving your walkie-talkie superpowers to always know the best way to talk, no matter what! So cool!
arXiv:2603.29796v1 Announce Type: new Abstract: Future wireless systems increasingly require predictive and transferable representations that can support multiple physical-layer (PHY) tasks under dynamic environments. However, most existing supervised learning-based methods are designed for a single task, which leads to high adaptation cost. To address this issue, we propose a joint-embedding predictive architecture for multimodal sensing-assisted communications (JEPA-MSAC), a self-supervised multimodal predictive representation learning framework for wireless environments. The proposed framework first maps multimodal sensing and communication measurements into a unified token space, and then pretrains a shared backbone using temporal block-masked JEPA to learn a predictive latent space th
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Abstract:Future wireless systems increasingly require predictive and transferable representations that can support multiple physical-layer (PHY) tasks under dynamic environments. However, most existing supervised learning-based methods are designed for a single task, which leads to high adaptation cost. To address this issue, we propose a joint-embedding predictive architecture for multimodal sensing-assisted communications (JEPA-MSAC), a self-supervised multimodal predictive representation learning framework for wireless environments. The proposed framework first maps multimodal sensing and communication measurements into a unified token space, and then pretrains a shared backbone using temporal block-masked JEPA to learn a predictive latent space that captures environment dynamics and cross-modal dependencies. After pretraining, the backbone is frozen and reused as a general future-feature generator, on top of which lightweight task heads are trained for localization, beam prediction, and received signal strength indicator (RSSI) prediction. Extensive experiments show the latent state supports accurate multi-task prediction with low adaptation cost. Additionally, ablation studies reveal its scaling behavior and the impact of key pretraining setups.
Comments: 13 pages, 10 figures
Subjects:
Signal Processing (eess.SP)
Cite as: arXiv:2603.29796 [eess.SP]
(or arXiv:2603.29796v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2603.29796
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Can Zheng [view email] [v1] Tue, 31 Mar 2026 14:29:42 UTC (2,203 KB)
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