Performance Analysis of IRS-Assisted NOMA Networks With Randomly Deployed Users

In this article, we investigate the intelligent reflecting surface (IRS)-assisted downlink nonorthogonal multiple access (NOMA) networks, where the users are randomly deployed in a disk. The randomly deployed users are divided into two groups, named center group and edge group, and then two distance-dependent user selection schemes are proposed. The developed closed-form expressions of outage probability and average rate for the proposed two user selection schemes in IRS-assisted NOMA networks are derived, as well as the asymptotic results at high signal-to-noise ratio (SNR) and the number of the reflecting number of the IRS trends to infinity, respectively. The asymptotic analysis results of outage probability for cell-edge user decay as lnSNR/SNRK+1, and the achieved diversity gain is K+1, at high SNR region, where K denotes the number of elements of the IRS. Furthermore, as K -> infinity, the asymptotic average rate of cell-edge user only depends on the power allocation factor of cell-center user. In addition, compared to IRS-assisted orthogonal multiple access networks, the IRS-assisted NOMA networks can always realize lower outage performance and higher average rate. Finally, Monte-Carlo simulation results are provided to verify the accuracy of the developed analytical results for the proposed two user selection schemes.