容量受限网络下用于预测性远程控制的时序JEPA
Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks
AI总结：
本文提出TS-JEPA框架，通过将高维感知数据编码为低维语义嵌入并结合信道感知调度，在容量受限网络中实现多设备的高效预测性远程控制。
摘要：
在远程控制系统中，当上行链路容量受限时（例如RedCap设备或大规模无线传感器网络），从无线传感器向远程控制器传输大量数据（如图像、视频帧）具有挑战性。此外，控制器通常只需要原始数据中信息丰富的表示。为解决这一问题，我们提出了一种语义驱动的预测控制与信道感知调度相结合的方法，以在有限网络容量下提升多设备的控制性能。其核心是所提出的框架，命名为时序联合嵌入预测架构（TS-JEPA），它在传感器端将高维感知数据编码为低维语义嵌入，从而降低通信开销。此外，TS-JEPA通过从当前嵌入和预测指令中预测未来嵌入来实现预测性推理，这些预测嵌入直接被语义执行器模型用于在嵌入空间内计算控制指令，从而无需重建原始数据。为进一步提高可靠性和通信效率，集成了信道感知调度，根据信道条件和信息年龄（AoI）动态优先安排设备传输。在倒立摆小车系统上的仿真表明，所提出的框架在通信效率、控制成本和预测精度方面显著优于传统控制基线。与传统调度方案相比，它能够在有限网络容量下实现鲁棒且可扩展的控制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
