# Communication-Control Codesign for Large-Scale Wireless Networked Control Systems

arXiv: https://arxiv.org/abs/2410.11316 · HTML: https://mlsanity.com/paper/2410.11316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 74 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems](https://mlsanity.com/paper/1809.05149/) (2018)
- [Deep Reinforcement Learning for Event-Triggered Control](https://mlsanity.com/paper/1809.05152/) (2018)
- [Learning Event-triggered Control from Data through Joint Optimization](https://mlsanity.com/paper/2008.04712/) (2020)
- [DRL-based Resource Allocation in Remote State Estimation](https://mlsanity.com/paper/2205.12267/) (2022)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Model-Free Design of Control Systems over Wireless Fading Channels](https://mlsanity.com/paper/2009.01751/) (2020)
- [LQG Control and Sensing Co-Design](https://mlsanity.com/paper/1802.08376/) (2018)
- [On the Latency, Rate and Reliability Tradeoff in Wireless Networked Control Systems for IIoT](https://mlsanity.com/paper/2007.00256/) (2020)
- [Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport](https://mlsanity.com/paper/2103.15260/) (2021)
- [Deep reinforcement learning for scheduling in large-scale networked control systems](https://mlsanity.com/paper/1905.05992/) (2019)
- [Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport](https://mlsanity.com/paper/2212.01958/) (2022)
- [A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attacks](https://mlsanity.com/paper/2103.05893/) (2021)
- [A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation](https://mlsanity.com/paper/2205.07070/) (2022)
- [Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control](https://mlsanity.com/paper/2305.08723/) (2023)
- [Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach](https://mlsanity.com/paper/2403.08398/) (2024)
- [Co-design of controller and routing redundancy over a wireless network](https://mlsanity.com/paper/1508.05217/) (2015)
- [Scheduling and control over networks using MPC with time-varying terminal ingredients: extended version](https://mlsanity.com/paper/1911.10780/) (2019)

## Abstract (arXiv)

Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots. The interdependence between communication and control requires integrated design, but traditional methods treat them separately, leading to inefficiencies. Current codesign approaches often rely on simplified models, focusing on single-loop or independent multi-loop systems. However, large-scale WNCSs face unique challenges, including coupled control loops, time-correlated wireless channels, trade-offs between sensing and control transmissions, and significant computational complexity. To address these challenges, we propose a practical WNCS model that captures correlated dynamics among multiple control loops with spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels. We formulate the codesign problem as a sequential decision-making task that jointly optimizes scheduling and control inputs across estimation, control, and communication domains. To solve this problem, we develop a Deep Reinforcement Learning (DRL) algorithm that efficiently handles the hybrid action space, captures communication-control correlations, and ensures robust training despite sparse cross-domain variables and floating control inputs. Extensive simulations show that the proposed DRL approach outperforms benchmarks and solves the large-scale WNCS codesign problem, providing a scalable solution for industrial automation.