Joint Jamming, Beamforming and IRS Reflecting Vector Optimization in an IRS-Assisted Secure NOMA Clustered Networks

Intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) network is considered to enhance convergence and wireless communications. However, eavesdropper can access an IRS reflection link, therefore, securing IRS-aided networks is an important challenge. In this paper, a secure IRS aided NOMA clustered network is proposed via artificial jamming. In each cluster, NOMA and jamming signals are sent using a multi-antenna base station to their users in the presence of an eavesdropper with assistance of IRS which has the capability of energy harvesting. The purpose of the paper is maximizing the sum-secrecy rate by proper selection of IRS, power allocation coefficients of the users in each cluster, optimizing the IRS reflecting vector, jamming and beamforming vectors with constraints on the quality of service requirement. We propose a low complexity iterative algorithm based on the convex optimization method and Karush–Kuhn–Tucker conditions to determine the best IRS as a relay for transmission and also the IRS reflecting, jamming and beamforming vectors. Simulation results are verified to show the effectiveness of the proposed scheme in different situations.