We consider a reconfigurable intelligent surface (RIS) aided coordinated multipoint (CoMP) transmission to enhance the achievable rates of the cell edge users (CEUs) in a downlink non‐orthogonal multiple access (NOMA) network enabled with simultaneous wireless information and power transfer (SWIPT). The RIS‐enabled SWIPT‐CoMP‐NOMA network improves the spectral efficiency and counterbalances the rate trade‐off introduced by SWIPT. The distribution model of the effective channel gain plays a vital role in ergodic capacity and outage probability analysis. In this article, we approximate the effective channel gains of the CEUs using the inverse Gaussian (IG) function and validate its accuracy using the well‐known Kolmogorov–Smirnov distance (KSD) test. Using the proposed channel gain model, we derive closed‐form expressions for the ergodic rates and outage probabilities of the CEUs. Using Monte‐Carlo simulations, we confirm the accuracy of the derived analytical expressions and show that the proposed IG function is more accurate than the Gamma function in estimating the outage probability. We also found that the proposed system always offers a higher ergodic rate than the SWIPT‐CoMP‐NOMA system.

The non‐orthogonal multiple access (NOMA) technique offers throughput improvement to meet the demands of the future generation of wireless communication networks. The objective of this work is to further improve the throughput by including an underlay cognitive radio network with an existing multi‐carrier NOMA network, using cooperative communication. The throughput is maximized by optimal resource allocation, namely, power allocation, subcarrier assignment, relay selection, user pairing, and subcarrier pairing. Optimal power allocation to the primary and secondary users is accomplished in a way that target rate constraints of the primary users are not affected. The throughput maximization is a combinatorial optimization problem, and the computational complexity increases as the number of users and/or subcarriers in the network increases. To this end, to reduce the computational complexity, a dynamic network resource allocation algorithm is proposed for combinatorial optimization. The simulation results show that the proposed network improves the throughput.