Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems
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Abstract (arXiv)
We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are necessary in enabling time-sensitive control systems to operate over wireless links with high reliability. Achieving fast data rates over wireless links thus comes at the cost of reliability in the form of high packet error rates compared to wired links due to channel noise and interference. However, the effect of the communication link errors on the control system performance depends dynamically on the control system state. We propose a novel control-communication co-design approach to the low-latency resource allocation problem. We incorporate control and channel state information to make scheduling decisions over time on frequency, bandwidth and data rates across the next-generation Wi-Fi based wireless communication links that close the control loops. Control systems that are closer to instability or further from a desired range in a given control cycle are given higher packet delivery rate targets to meet. Rather than a simple priority ranking, we derive precise packet error rate targets for each system needed to satisfy stability targets and make scheduling decisions to meet such targets while reducing total transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS) method is tested in numerous simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2018.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 1.
7 papers.
- 2024Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networkscited by 9
- 2022Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approachcited by 7
- 2021Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Controlcited by 5
- 2021Adaptive Scheduling for Machine Learning Tasks over Networkscited by 2
- 2021Linear Regression over Networks with Communication Guaranteescited by 2
- 2022A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocationcited by 2
- 2021Federated Reinforcement Learning at the Edgecited by 1
- 2024Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks7 Jun 2024
- 2022Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach3 Oct 2022
- 2022A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation14 May 2022
- 2021Federated Reinforcement Learning at the Edge11 Dec 2021
- 2021Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control26 Sep 2021
- 2021Linear Regression over Networks with Communication Guarantees6 Mar 2021
- 2021Adaptive Scheduling for Machine Learning Tasks over Networks25 Jan 2021