Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks
It addresses system-level optimization for autonomous operations in hazardous environments, representing an incremental improvement in resource management for 6G networks.
This paper tackles the joint optimization of sensor-actuator pairing and resource allocation for SC3 closed loops in 6G networks, developing a learning-optimization-integrated actor-critic framework that achieves near-optimal solutions with low computational complexity and reduces control cost.
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.