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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.23217 |
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| _version_ | 1866915887272951808 |
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| author | Fang, Xinran Feng, Wei Wang, Yanmin Chen, Yunfei Ren, Baoquan Ge, Ning Jin, Shi |
| author_facet | Fang, Xinran Feng, Wei Wang, Yanmin Chen, Yunfei Ren, Baoquan Ge, Ning Jin, Shi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23217 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks Fang, Xinran Feng, Wei Wang, Yanmin Chen, Yunfei Ren, Baoquan Ge, Ning Jin, Shi Information Theory 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. |
| title | Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks |
| topic | Information Theory |
| url | https://arxiv.org/abs/2603.23217 |