Time-Series JEPA for Predictive Remote Control Under Capacity-Limited Networks

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). Extensive simulations on inverted cart-pole systems demonstrate that the proposed framework achieves a 98.95% reduction in communication cost, a normalized prediction error of 0.004, and 74.48% control accuracy, outperforming conventional control baselines. Furthermore, the proposed framework maintains robust control performance on 15-step prediction horizons and supports up to 16× more devices than round-robin and opportunistic scheduling schemes with conventional control baselines under similar network conditions.