In remote control systems, transmitting large data volumes (e.g. video feeds) from wireless sensors to faraway controllers is challenging when the uplink channel capacity is limited (e.g. RedCap devices or massive wireless sensor networks). Furthermore, the controllers often only need the information-rich components of the original data. To address this, we propose a Time-Series Joint Embedding Predictive Architecture (TS-JEPA) and a semantic actor trained through self-supervised learning. This approach harnesses TS-JEPA's semantic representation power and predictive capabilities by capturing spatio-temporal correlations in the source data. We leverage this to optimize uplink channel utilization, while the semantic actor calculates control commands directly from the encoded representations, rather than from the original data. We test our model through multiple parallel instances of the well-known inverted cart-pole scenario, where the approach is validated through the maximization of stability under constrained uplink channel capacity.

在远程控制系统中，当上行通道容量受限时（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数据量（如视频流）是具有挑战性的。为了解决这个问题，本研究提出了一种基于时间序列联合嵌入预测架构（TS-JEPA）和通过自监督学习训练的语义参与者的方法。该方法利用TS-JEPA的语义表示能力和预测能力，捕捉源数据中的时空相关性，从而优化上行通道利用率，而语义参与者直接从编码表示中计算控制命令，而非从原始数据中计算。通过对知名倒立摆场景的多个并行实例进行测试，验证了该方法在受限上行通道容量下最大化稳定性的有效性。

基于容量受限网络的时序预测远程控制