Enhancing Computation Offloading by Integrating MEC and IRS

Integrating Mobile Edge Computing (MEC) and Intelligent Reflecting Surfaces (IRS) promises to revo-lutionize wireless computing networks by supporting new latency-sensitive applications and computation-intensive tasks, e.g., industrial Internet of Things automation in Industry 5.0. MEC enhances the devices' computational capability, increasing computational energy efficiency and reducing latency, while IRS optimizes signal propagation. This paper studies how to improve the computational capabilities of low-power devices through computation offloading based on the integration of MEC and IRS. To achieve the desired objective, we formulate a joint optimization problem that is non-linear, non-convex, and NP-hard in nature; therefore, to achieve the most efficient solution, we employ the block coordinate descent (BCD) approach that decouples the original optimization problem into a series of subproblems and solves them iteratively using the convex optimization toolbox, e.g., CVX. Moreover, numer-ical results demonstrate a fundamental tradeoff between local, binary, and partial offloading schemes, which reveals that the proposed scheme outperforms the benchmark and indicates its effectiveness in next-generation wireless communication. Our analysis demonstrates the scheme's exceptional stability and energy efficiency, particularly in the hybrid IRS mode. We also highlight the adaptability of our approach to various data sizes.