Jamming-Resilient Fairness-Oriented Resource Allocation Technique for IRS-Assisted NOMA 6G-Enabled IoT Networks

Abstract

Intelligent Reflecting Surface (IRS) has recently been combined with cutting-edge technologies to meet the demanding requirements of six-generation (6G)-based IoT consumer electronics (CE) communication systems. This paper considers IRS-assisted hybrid orthogonal frequency division multiple access (OFDMA) and non-orthogonal multiple access (NOMA) systems under proactive jamming attacks. Jamming severely affects the performance of CE devices, reducing data rates, increasing packet loss, and significantly reducing communication reliability. A jamming-aware fairness-oriented design is proposed to overcome such attacks and maintain fairness between CE devices. Specifically, the fairness index (FI) is maximized under relevant constraints, including secure transmission requirements and transmission power constraints. However, due to the non-convex and fractional nature of the proposed jamming-aware FI optimization framework, an iterative algorithm is developed to solve the problem and evaluate the optimization parameters, namely the IRS phase reflection coefficients and the per-user allocated power level (i.e., CE device). To validate the effectiveness of the proposed jamming-aware FI maximization framework, its performance is compared with a set of benchmarks. The simulation results demonstrate its superiority in ensuring fairness among users and providing secure jamming-resistant communication in IRS-assisted OFDMA-NOMA CE-based systems.