Enhancing Fog Computing through Intelligent Reflecting Surface Assistance: A Lyapunov Driven Reinforcement Learning Approach

The explosive growth of mobile devices in the Internet of Things (IoT) has significantly increased the demand for fog computing (FC). As a key component in optimizing wireless communication environments, intelligent reflecting surfaces (IRS) have garnered considerable attention. This paper develops an online optimization model for IRS-assisted FC, implemented across multiple cells with computational nodes. We propose a Lyapunov-function-based, space aggregation-aided proximal policy optimization (LSAPPO) algorithm to address the challenges of online optimization in IRS-assisted FC environments. Our approach introduces a reinforcement learning algorithm that employs a proximal policy optimization (PPO) agent, enhanced by Lyapunov drift-plus-penalty Optimization. The space aggregation method efficiently consolidates excessive channel state information (CSI) and decision variables into a manageable set of parameters, thereby simplifying the computational framework. Numerical results demonstrate that our algorithm outperforms established benchmarks, highlighting its effectiveness in complex wireless environments.