Resource Control in IRS Assisted Multi-Access Edge Computing for Sustainable 6G IIoT Networks

Ashu Taneja, Shalli Rani, Wadii Boulila

Abstract: Industrial Internet-of-Things (IIoT) applications in sectors such as energy, manufacturing, healthcare, transportation, and logistics employ intelligent devices, sensors, and connected terminals to improve operational efficiency. However, the IIoT ecosystem faces communication and computation challenges due to limited connectivity and network resources. Edge computing resources help reduce network congestion. This paper introduces a sixth-generation (6G) communication framework that integrates intelligent reflecting surfaces (IRSs) with non-orthogonal multiple access (NOMA) for mobile edge computing (MEC) systems. The IRS-NOMA approach enables multiple users to offload their tasks simultaneously by adapting the communication environment, thus minimizing offloading delays. Additionally, we propose a resource control algorithm that assigns cell-edge user clusters to specific IRSs based on optimal IRS phase shift and channel correlation criteria. The system outage probability and achievable rate are derived and supported by a comprehensive mathematical analysis. Results indicate that the proposed approach achieves an outage probability of $10^{-5}$ for a transmit power P of 20 dBm with IRS reflecting elements $N = 64$ . Moreover, the achievable rate reaches 5.6 bps/Hz at $P = 20$ dBm. A comparison with two conventional baseline approaches is also provided.