Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks

TL;DR

This paper proposes a learning-optimization framework for joint task orchestration and resource allocation in 6G network environments, improving efficiency and reducing costs in autonomous sensor-actuator systems.

Contribution

It introduces the LOAC framework that combines deep learning and optimization to solve complex joint pairing and resource allocation problems in SC3 closed loops.

Findings

Achieves near-optimal solutions with low computational complexity.

Significantly reduces control costs in simulations.

Demonstrates effectiveness of learning-optimization in network resource management.

Abstract

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.