# Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks

arXiv: https://arxiv.org/abs/2406.04853 · HTML: https://mlsanity.com/paper/2406.04853/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 23 of 23)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning](https://mlsanity.com/paper/2004.14646/) (2020)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [A-JEPA: Joint-Embedding Predictive Architecture Can Listen](https://mlsanity.com/paper/2311.15830/) (2023)
- [Rethinking Modern Communication from Semantic Coding to Semantic Communication](https://mlsanity.com/paper/2110.08496/) (2021)
- [Semantics-Native Communication with Contextual Reasoning](https://mlsanity.com/paper/2108.05681/) (2021)
- [Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)](https://mlsanity.com/paper/1810.06938/) (2018)
- [Massive Machine-type Communications in 5G: Physical and MAC-layer solutions](https://mlsanity.com/paper/1606.03893/) (2016)
- [Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems](https://mlsanity.com/paper/1811.00409/) (2018)
- [6G Cellular Networks and Connected Autonomous Vehicles](https://mlsanity.com/paper/2010.00972/) (2020)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Optimal Sensor Scheduling for Multiple Linear Dynamical Systems](https://mlsanity.com/paper/1604.04158/) (2016)
- [Predictive Control and Communication Co-Design: A Gaussian Process Regression Approach](https://mlsanity.com/paper/2003.00243/) (2020)
- [Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning](https://mlsanity.com/paper/1906.06225/) (2019)

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

- [Joint Embeddings Go Temporal](https://mlsanity.com/paper/2509.25449/) (2025)
- [Learning Latent Wireless Dynamics from Channel State Information](https://mlsanity.com/paper/2409.10045/) (2024)
- [TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations](https://mlsanity.com/paper/2504.12721/) (2025)
- [From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management](https://mlsanity.com/paper/2506.16216/) (2025)
- [Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks](https://mlsanity.com/paper/2602.07000/) (2026)
- [Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?](https://mlsanity.com/paper/2609.31680/) (2026)
- [BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series](https://mlsanity.com/paper/2610.05409/) (2026)
- [Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition](https://mlsanity.com/paper/2610.15891/) (2026)
- [RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control](https://mlsanity.com/paper/2610.17584/) (2026)

## Abstract (arXiv)

In remote control systems, transmitting large data volumes (e.g., images, video frames) from wireless sensors to remote controllers is challenging when uplink capacity is limited (e.g., RedCap devices or massive wireless sensor networks). Furthermore, controllers often need only information-rich representations of the original data. To address this, we propose a semantic-driven predictive control combined with a channel-aware scheduling to enhance control performance for multiple devices under limited network capacity. At its core, the proposed framework, coined Time-Series Joint Embedding Predictive Architecture (TS-JEPA), encodes high-dimensional sensory data into low-dimensional semantic embeddings at the sensor, reducing communication overhead. Furthermore, TS-JEPA enables predictive inference by predicting future embeddings from current ones and predicted commands, which are directly used by a semantic actor model to compute control commands within the embedding space, eliminating the need to reconstruct raw data. To further enhance reliability and communication efficiency, a channel-aware scheduling is integrated to dynamically prioritize device transmissions based on channel conditions and age of information (AoI). Simulations on inverted cart-pole systems show that the proposed framework significantly outperforms conventional control baselines in communication efficiency, control cost, and predictive accuracy. It enables robust and scalable control under limited network capacity compared to traditional scheduling schemes.