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LatentWave introduces a Joint-Embedding Predictive Architecture (JEPA) for wireless foundation models that mitigates the low-level signal bias inherent in traditional masked reconstruction methods. By predicting masked regions in latent space rather than raw inputs, the model achieves superior transferability across diverse tasks like RF classification and 5G positioning while accommodating variable antenna configurations. The study further demonstrates that masking geometry acts as a tunable inductive bias, where frequency masking optimizes channel-related tasks and region masking enhances signal discriminability.
LatentWave: JEPA Pretraining for Wireless Foundation Models
arXiv:2606.06373v1 Announce Type: cross Abstract: Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task. However, existing approaches rely on masked input reconstruction, which can bias representations toward low-level signal details. In this paper, we propose LatentWave, a wireless foundation model pretrained using a Joint-Embedding Predictive Architecture (JEPA) on diverse wireless spectrograms and channel state information (CSI). By predicting masked regions in latent space, LatentWave learns representations that are more transferable out of the box across diverse downstream tasks. The proposed architecture employs per-channel patch embeddings with stochastic channel sampling during pretraining, allowing it to process variable antenna counts and improving usability across heterogeneous wireless configurations. We evaluate LatentWave on four downstream tasks: RF signal classification, 5G NR positioning, beam prediction, and LoS/NLoS classification, comparing against a masked-modeling baseline (WavesFM) pretrained on the same data. Additionally, we show that the masking geometry introduces a task-dependent inductive bias: frequency masking strongly favors channel-related tasks such as positioning and beam prediction, while region masking better preserves discriminability for signal classification.
Abstract:Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task. However, existing approaches rely on masked input reconstruction, which can bias representations toward low-level signal details. In this paper, we propose LatentWave, a wireless foundation model pretrained using a Joint-Embedding Predictive Architecture (JEPA) on diverse wireless spectrograms and channel state information (CSI). By predicting masked regions in latent space, LatentWave learns representations that are more transferable out of the box across diverse downstream tasks. The proposed architecture employs per-channel patch embeddings with stochastic channel sampling during pretraining, allowing it to process variable antenna counts and improving usability across heterogeneous wireless configurations. We evaluate LatentWave on four downstream tasks: RF signal classification, 5G NR positioning, beam prediction, and LoS/NLoS classification, comparing against a masked-modeling baseline (WavesFM) pretrained on the same data. Additionally, we show that the masking geometry introduces a task-dependent inductive bias: frequency masking strongly favors channel-related tasks such as positioning and beam prediction, while region masking better preserves discriminability for signal classification.
Signal Processing (eess.SP); Artificial Intelligence (cs.AI)
arXiv:2606.06373 [eess.SP]
(or arXiv:2606.06373v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2606.06373 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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