# Adaptive Clustering of Users in Power Domain NOMA
###### Abstract  
By enabling multiple non-orthogonal transmissions, power domain non-orthogonal multiple access (PD-NOMA) potentially increases a system's spectral efficiency. This technique can become an alternative for future generations of wireless communication networks. The efficiency of this method fundamentally depends on two previous processing steps: an appropriate grouping of users (transmission candidates) as a function of the channel gains and the choice of power levels that will be used to transmit each signal. Thus far, the solutions presented in the literature to address the problems of user clustering and power allocation do not consider the dynamics of communication systems, i.e., the temporal variation in the number of users and the channel conditions. In order to consider these dynamic characteristics in the clustering of users in NOMA systems, this work proposes a new clustering technique based on a modification of the DenStream evolutionary algorithm, chosen for its evolutionary capacity, noise robustness and online processing. We evaluated the performance of the proposed clustering technique considering, for simplicity, the use of an already widely known power allocation strategy called improved fractional strategy power allocation (IFSPA). The results show that the proposed clustering technique can follow the system dynamics, clustering all users and favoring the uniformity of the transmission rate between the clusters. Compared to orthogonal multiple access (OMA) systems, the proposed model's gain was approximately 10%, obtained on a challenging communication scenario for NOMA systems since the channel model adopted does not favor a large difference in the channel gains between users. n 202323314  
non-orthogonal multiple access, NOMA supports more users per orthogonal resources available in the system. Thus, it significantly improves the spectral efficiency of mobile communication systems [8]. NOMA systems can be classified into two categories [9]: power domain NOMA (PD-NOMA), considered in this work, and code domain NOMA (CD-NOMA). Although both applications in communications networks are relatively new, many approaches have already been proposed [10; 11; 12; 13]. Power domain NOMA uses two techniques already known in the literature, superposition coding (SC) and successive interference cancellation (SIC), to enable the transmission of more than one user simultaneously using the same time-frequency-code resource. However, the success of interference cancellation in the receivers depends directly on power allocation schemes and user clustering. A more significant difference in channel gains between grouped users favors a higher global transmission rate of the system. Specifically, when allocating more power to those users with worse channel conditions, it is possible to increase the global transmission rate of the system. Thus, a careful adjustment in the users' power allocation and the decision of how the users share the system resources (defined by the clusterization method) allows the base station to be able to control, with flexibility, the general data rate, the cell edge data rate and ensure user fairness [2]. In fact, for a NOMA system to achieve its best performance, it would be necessary to evaluate all possible combinations of users in clusters and their respective power levels, which would be infeasible in computational terms. In recent years [14; 15; 16; 17; 18], some works on NOMA have focused on sub-optimal solutions for both problems, seeking to improve the system's spectral efficiency and reduce interference between users. In order to mitigate the multiple access interference in downlink NOMA, an approach based on beamforming was employed in [19]. Each cluster can be dealt with independently with the inter-cluster interference suppressed by beamforming. Thus, the problem is reduced to finding the optimal beamforming vector to minimize the transmit power for a given quality of service (QoS) in terms of target signal-to-interference-plus-noise ratios (SINRs) or transmission rates.