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

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.

Figures (13)

• Figure 1: Illustration of a 6G-enabled autonomous operation system for disaster rescue: (a) normal state, (b) emergency detection and preparation, and (c) on-site deployment and rescue operation.
• Figure 2: Illustration of a 6G autonomous operation system, where the EIH orchestrates distributed sensors and actuators into integrated $\mathbf{SC}^3$ closed loops. As depicted in the upper left, the $\mathbf{SC}^3$ closed loop is conceptually analogous to an assembled robot: operating as a unified entity, it gathers environmental data through its sensor-based eyes, processes information within its EIH-based brain, and executes precise control commands through its actuator-based hands.
• Figure 3: Illustration of the $\mathbf{SC}^3$ closed-loop model from three levels: physical level, signal level, and information level.
• Figure 4: Comparison between the achievable LQR cost, computed via the SDP method, and the theoretical lower bound. The parameters are set as follows: $\mathbf{A}=\mathrm{diag}(2, 1.2, 0.3)$, $\mathbf{B}=\mathbf{I}_{3}$, $\mathbf{Q}=\mathbf{I}_{3}$, $\mathbf{R}=\mathbf{I}_{3}$, and covariance $\Sigma_{\mathbf{v}}=\mathbf{I}_{3}$.
• Figure 5: The structure of the proposed LOAC framework, which includes a DNN-based actor and an optimization-based critic.

Theorems & Definitions (1)

• Theorem 1