JEPA for AI-Native 6G: Predictive Representations and Open Challenges

2026-07-09

AI Summary

This work addresses key challenges in AI-native 6G networks—namely, scarce labeled data, multi-source heterogeneity, partial observability, and non-stationary channels—by proposing a self-supervised pretraining approach based on the Joint-Embedding Predictive Architecture (JEPA). The method learns a universal predictive representation layer applicable across 6G RAN, O-RAN, and core networks by predicting masked representations of channel states, beam measurements, KPIs, topology, and sensing data in a latent space. To enhance robustness and label efficiency under distribution shifts, future beam energy is incorporated as an auxiliary prediction target. Evaluated on beam management tasks, the proposed framework demonstrates superior cross-domain sample efficiency and generalization compared to supervised learning, offering a scalable foundation for intelligent 6G infrastructure.

Abstract

Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core. This transition requires learning from limited labels, heterogeneous wireless and network data, partial observations, non-stationary propagation, and latency-constrained control loops. Joint-embedding predictive architecture (JEPA) is a promising self-supervised paradigm for this setting because it predicts missing or future representations in latent space instead of reconstructing raw measurements or using contrastive negative samples. This article presents a wireless-oriented tutorial on JEPA for 6G intelligence. We define the JEPA training mechanism, describe how CSI, beam measurements, KPIs, topology graphs, and sensing observations can be tokenized and masked, and position the learned encoder as a predictive representation layer for RAN, O-RAN, edge, and core functions, with task-specific heads or controllers producing final decisions. Then we present an illustrative, beam-management case study suggesting that a wireless-aware target, specifically an auxiliary future beam-energy target during self-supervised pretraining, can improve label efficiency and robustness across shifted deployment conditions relative to a supervised source domain. Finally, we outline open challenges in multi-timescale prediction, action-conditioned modeling, distributed training, trustworthiness, efficient deployment, benchmarking, and standardization.

Problem

• AI-native 6G
• self-supervised learning
• predictive representations
• limited labels
• non-stationary propagation

Innovation

• JEPA
• AI-native 6G
• predictive representations
• self-supervised learning
• beam management