### Prior Work
In addition, the authors implemented OFDMA and NOMA in a hybrid manner, and the results proved that the hybrid technique outperformed the results achieved with standalone OFDMA or NOMA for efficient user association. Similarly, in [16], the author considered the gained sum rate of NOMA over OFDMA for uplink transmission with a single antenna, multiple antennas, and massive multiple antennas and proved with analytical results the accuracy of the gain achieved with the proposed technique. In [17], the authors showed with simulation results that NOMA can be a solution to increase the cell capacity without compromising the network performance for 5G networks, eventually satisfying users' ever-increasing data rate demands. The benefits of both OFDMA and NOMA schemes cannot be ignored for 5G ultra-dense heterogeneous networks. PD-NOMA has been suggested for user association as a technique for achieving a reliable and efficient solution with improved network capacity. Therefore, it can be concluded from the above-discussed literature that PD-NOMA has gained great significance in radio resource management and user association in heterogeneous networks. However, it still has to mitigate a few challenges with ultra-dense heterogeneous network deployments. Other popular techniques are used with PD-NOMA, including the cooperative NOMA and cognitive radio with the NOMA technique. In cooperative NOMA, the relaying function is performed by the near user to reduce the outage probability and can virtually extend the coverage area of the base station, and the relaying function can be performed under various techniques to achieve the desired results of improved network performance [18]. Further, in [19] the author presented a detailed critical overview of current research methods in cooperative PD-NOMA (PD-CNOMA) and also discussed the implementation of PD-CNOMA with other techniques such as cognitive radio, energy harvesting, and full duplex 5G technologies. Further, in [20], the author has explored energy harvesting assisted cooperative NOMA with underlay cognitive radio networks. The presented methodology has high complexity, and the impacts of power transmission, energy harvesting co-efficient, and imperfect SIC was also analyzed with simulation and analytical results. Thus, it can be concluded that by using cooperative PD-NOMA, the performance of 5G networks can further be improved, i.e., by implementing better techniques for deploying relays and by selecting significant values of transmission power, SIC strategies, and other performance parameters. In cognitive radio with NOMA, on the other hand, the far user is considered the primary user if the target rate conditions are satisfied with the far user. Thus, more power is allocated to the far user, and remaining power is allocated to the secondary user; the coverage probability can be improved with this scheme, but with high signal to noise ratio (SNR) values, the system performance declines [21]. Hence, with the above-mentioned schemes, the user association can be improved, but still the complexity of the SIC process at the receiver's end persists. With clustering, the complexity of the SIC process can be reduced, and thus the chances of errors during the SIC process can be reduced [22; 23]. In this paper, we proposed the clustered PD-NOMA technique and implemented it with the previously applied interference managed hybrid clustering (IMHC) scheme [1] and found that with the proposed PD-NOMA scheme, the interference among the users was further reduced because with the clustered PD-NOMA, the frequency band was shared with a lower number of users due to clustering. Hence, we could achieve improved throughput and channel capacity with the proposed scheme due to effectively reduced interference among the associated users and high channel gain differences. The literature analyzed in Table 1 and discussed above shows that the performance of PD-NOMA with massive deployments, specifically if the nodes are deployed over more considerable distances, needs to be addressed.