In hazardous environments, sensors and actuators can be deployed to see and operate on behalf of humans, enabling safe and efficient task execution. Functioning as a neural center, the edge information hub (EIH), which integrates communication and computing capabilities, coordinates these sensors and actuators into sensing-communication-computing-control (SC3) closed loops to enable autonomous operations. From a system-level optimization perspective, this paper addresses the problem of joint sensor-actuator pairing and resource allocation across multiple SC3 closed loops. To tackle the resulting mixed-integer nonlinear programming problem, we develop a learning-optimization-integrated actor-critic (LOAC) framework. In this framework, a deep neural network-based actor generates pairing candidates, while an optimization-based critic subsequently allocates communication and computing resources. The actor is then iteratively refined through feedback from the critic. Simulation results demonstrate that the LOAC framework achieves near-optimal solutions with low computational complexity, offering significant performance gains in reducing control cost.
@article{arxiv.2603.23217,
title = {Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks},
author = {Xinran Fang and Wei Feng and Yanmin Wang and Yunfei Chen and Baoquan Ren and Ning Ge and Shi Jin},
journal= {arXiv preprint arXiv:2603.23217},
year = {2026}
}