Performance Analysis of Cognitive Underlay NOMA Random Networks Under Nakagami-m Channel

This study addresses the performance evaluation of cognitive non-orthogonal multiple access (CONOMA) networks. Specifically, we focus on cognitive underlay networks employing non-orthogonal multiple access (NOMA), where all secondary users (SUs) are characterized using tools from stochastic geometry. In addition, we utilize the Nakagami-m distribution, which offers a more versatile model compared to the conventional Rayleigh or Rician distributions, applicable across various transmission environments. In this context, we derive closed-form expressions for the outage probability of both favorable and unfavorable users. The selection criteria for identifying favorable and unfavorable users are based on large-scale path-loss considerations. By doing so, we aim to provide a comprehensive understanding of the network performance under different conditions. Finally, we present simulation results obtained through Monte-Carlo simulations to illustrate and validate our findings.