# Wireless Network Design for Control Systems: A Survey

arXiv: https://arxiv.org/abs/1708.07353 · HTML: https://mlsanity.com/paper/1708.07353/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 13 of 13)

- [Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2407.15784/) (2024)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Structure-Enhanced DRL for Optimal Transmission Scheduling](https://mlsanity.com/paper/2212.12704/) (2022)
- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2311.16895/) (2023)
- [Decision-focused Graph Neural Networks for Combinatorial Optimization](https://mlsanity.com/paper/2406.03647/) (2024)
- [To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing](https://mlsanity.com/paper/2209.02166/) (2022)
- [Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling](https://mlsanity.com/paper/2211.10827/) (2022)
- [Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees](https://mlsanity.com/paper/2507.08653/) (2025)
- [A Deep Learning Approach To Estimation Using Measurements Received Over a Network](https://mlsanity.com/paper/2201.08020/) (2022)
- [Wireless Human-Machine Collaboration in Industry 5.0](https://mlsanity.com/paper/2410.14153/) (2024)
- [Observation Compression in Rate-Limited Closed-Loop Distributed ISAC Systems: From Signal Reconstruction to Control](https://mlsanity.com/paper/2505.01780/) (2025)
- [Computing the Exact Pareto Front in Average-Cost Multi-Objective Markov Decision Processes](https://mlsanity.com/paper/2604.02196/) (2026)

## Abstract (arXiv)

Wireless networked control systems (WNCS) are composed of spatially distributed sensors, actuators, and con- trollers communicating through wireless networks instead of conventional point-to-point wired connections. Due to their main benefits in the reduction of deployment and maintenance costs, large flexibility and possible enhancement of safety, WNCS are becoming a fundamental infrastructure technology for critical control systems in automotive electrical systems, avionics control systems, building management systems, and industrial automation systems. The main challenge in WNCS is to jointly design the communication and control systems considering their tight interaction to improve the control performance and the network lifetime. In this survey, we make an exhaustive review of the literature on wireless network design and optimization for WNCS. First, we discuss what we call the critical interactive variables including sampling period, message delay, message dropout, and network energy consumption. The mutual effects of these communication and control variables motivate their joint tuning. We discuss the effect of controllable wireless network parameters at all layers of the communication protocols on the probability distribution of these interactive variables. We also review the current wireless network standardization for WNCS and their corresponding methodology for adapting the network parameters. Moreover, we discuss the analysis and design of control systems taking into account the effect of the interactive variables on the control system performance. Finally, we present the state-of-the-art wireless network design and optimization for WNCS, while highlighting the tradeoff between the achievable performance and complexity of various approaches. We conclude the survey by highlighting major research issues and identifying future research directions.