# Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems

arXiv: https://arxiv.org/abs/2311.16895 · HTML: https://mlsanity.com/paper/2311.16895/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [Deep Reinforcement Learning for Event-Triggered Control](https://mlsanity.com/paper/1809.05152/) (2018)
- [Wireless Network Design for Control Systems: A Survey](https://mlsanity.com/paper/1708.07353/) (2017)
- [Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario](https://mlsanity.com/paper/1911.13050/) (2019)
- [A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions](https://mlsanity.com/paper/2009.12164/) (2020)
- [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)
- [Joint Pilot and Payload Power Allocation for Massive-MIMO-enabled URLLC IIoT Networks](https://mlsanity.com/paper/1912.12438/) (2019)
- [Age of Loop for Wireless Networked Control Systems Optimization](https://mlsanity.com/paper/2106.00415/) (2021)
- [End-to-End Reliability-aware Scheduling for Wireless Sensor Networks](https://mlsanity.com/paper/1412.2546/) (2014)
- [Intelligent Reflecting Surface-aided URLLC in a Factory Automation Scenario](https://mlsanity.com/paper/2103.09323/) (2021)
- [Effect of Computational Power of Sensors on Event-Triggered Control Mechanisms over a Shared Contention-Based Network](https://mlsanity.com/paper/2104.03076/) (2021)

## Built on by (most cited first, 3 of 3)

- [Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2407.15784/) (2024)
- [Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees](https://mlsanity.com/paper/2507.08653/) (2025)
- [Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning](https://mlsanity.com/paper/2507.12031/) (2025)

## Abstract (arXiv)

The design of Wireless Networked Control System (WNCS) requires addressing critical interactions between control and communication systems with minimal complexity and communication overhead while providing ultra-high reliability. This paper introduces a novel optimization theory based deep reinforcement learning (DRL) framework for the joint design of controller and communication systems. The objective of minimum power consumption is targeted while satisfying the schedulability and rate constraints of the communication system in the finite blocklength regime and stability constraint of the control system. Decision variables include the sampling period in the control system, and blocklength and packet error probability in the communication system. The proposed framework contains two stages: optimization theory and DRL. In the optimization theory stage, following the formulation of the joint optimization problem, optimality conditions are derived to find the mathematical relations between the optimal values of the decision variables. These relations allow the decomposition of the problem into multiple building blocks. In the DRL stage, the blocks that are simplified but not tractable are replaced by DRL. Via extensive simulations, the proposed optimization theory based DRL approach is demonstrated to outperform the optimization theory and pure DRL based approaches, with close to optimal performance and much lower complexity.