Deep learning based resource allocation for ultra-reliable communications in wireless control systems

Wireless Networked Control Systems (WNCSs) play an important role in fifth-generation (5G) and sixth-generation (6G) networks to support mission-critical applications, such as Internet of Things (IoT), Remote Driving, and Collaborative Robots (Cobots). WNCS design demands consideration of both control and communication systems requirements to guarantee the broadcasting of reliable control commands at low latency from the controller to the actuators. In the first part of the thesis, a joint optimization of control and communication systems in the Finite Blocklength (FBL) regime is devised with the objective of minimizing the total power consumption by optimizing the sampling period of the control system, blocklength, and packet error probability of the communication system. Then, the optimization framework is simplified using optimality conditions to only one decision variable of blocklength. Then, the new optimization problem is fed to an online Deep Reinforcement Learning (DRL) algorithm to be trained, and the changing wireless environment is learned, executing optimal results. Second, a diffusion model, specifically the Denoising Diffusion Probabilistic Model (DDPM), is proposed to allocate resources for WNCSs. The optimization framework is utilized to collect a dataset of Channel State Information (CSI) and its corresponding optimal blocklength values. Then, the dataset is used to train the DDPM-based model to learn the complex distribution of the solution and the environmental parameters and generate optimal blocklength values based on the CSI as conditional information. The proposed schemes perform close to the optimization theory-based solution and outperform the previously proposed benchmarks, demonstrating superior performance in total power consumption and avoiding critical constraint violations.