Beamforming Design and Resource Allocation for IRS-Assisted NOMA Cognitive Radio System

Abstract

In this work, we have studied an intelligent reflective surface (IRS)-assisted cognitive radio system. The non-orthogonal multiple access (NOMA) has been deployed to strengthen the spectrum sharing behaviour between two secondary users. In order to improve the secondary network in a fairness manner without significantly interfering the primary network, we formulate a minimum throughput minimization problem for the secondary network via a joint design of IRS beamforming and resource allocation at the secondary transmitter. To address the complex non-convex problem, we adopt the successive convex approximation technique and propose an iterative algorithm for alternating improving the IRS beamforming scheme and the resource allocation design. The algorithm will finally converge to an efficient suboptimal solution. Through numerical results, we highlight the advantage of deploying IRS in the considered system and also the potential superiority of NOMA scheme over the space-division multiple access (SDMA) via comparisons.