  Diffusion models are vastly used in generative AI, leveraging their
capability to capture complex data distributions. However, their potential
remains largely unexplored in the field of resource allocation in wireless
networks. This paper introduces a novel diffusion model-based resource
allocation strategy for Wireless Networked Control Systems (WNCSs) with the
objective of minimizing total power consumption through the optimization of the
sampling period in the control system, and blocklength and packet error
probability in the finite blocklength regime of the communication system. The
problem is first reduced to the optimization of blocklength only based on the
derivation of the optimality conditions. Then, the optimization theory solution
collects a dataset of channel gains and corresponding optimal blocklengths.
Finally, the Denoising Diffusion Probabilistic Model (DDPM) uses this collected
dataset to train the resource allocation algorithm that generates optimal
blocklength values conditioned on the channel state information (CSI). Via
extensive simulations, the proposed approach is shown to outperform previously
proposed Deep Reinforcement Learning (DRL) based approaches with close to
optimal performance regarding total power consumption. Moreover, an improvement
of up to eighteen-fold in the reduction of critical constraint violations is
observed, further underscoring the accuracy of the solution.
