# Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems

arXiv: https://arxiv.org/abs/1811.00409 · HTML: https://mlsanity.com/paper/1811.00409/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks](https://mlsanity.com/paper/2406.04853/) (2024)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [Adaptive Scheduling for Machine Learning Tasks over Networks](https://mlsanity.com/paper/2101.10007/) (2021)
- [Linear Regression over Networks with Communication Guarantees](https://mlsanity.com/paper/2103.04140/) (2021)
- [A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation](https://mlsanity.com/paper/2205.07070/) (2022)
- [Federated Reinforcement Learning at the Edge](https://mlsanity.com/paper/2112.05908/) (2021)

## 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.