## 1 Introduction
### Prior Works
Moreover, the user pairing for a cooperative NOMA transmission system was examined in [12], in which two users experiencing significantly different channel gains are grouped together based on the sorted users' channel gains in ascending order. It was demonstrated in the same work, that such pairing of users in a NOMA cluster provides significant performance gain over other existing UC methods as well as the OMA. However, instead of using the optimal power allocation, the authors only considered the fixed power allocation in their work. In another study, two novel UC schemes that include a centralized UC and a distributed UC were proposed [13] in which users are sorted based on the large-scale fading (LSF) gain to enhance the performance of the NOMA system. Particularly, in the centralized UC, the primary user with the highest LSF gain is selected and paired with the complementary user that is selected based on the principle of signal difference alignment (SDA). In contrast, in the distributed UC, the primary user is selected similar to that of the centralized UC and the complementary user is selected based on the zero-forcing vector achieved by the primary user. The work in [13] analytically showed that a NOMA utilizing the proposed centralized and distributed UC techniques can achieve a much better throughput performance than the conventional UC schemes. However, these conventional UC schemes only exploit the channel gains, but the distribution of users in each cluster is not taken into account. The performance of NOMA under dynamic UC (DUC) was proposed in [14] to maximize the sum-throughput performance of a NOMA system by solving the formulated mixed-integer non-linear programming problem based on the Karush-Kuhn-Tucker (KKT) optimality conditions. In this work, the UC has been extended from grouping a pair of users to grouping three and four users in a cluster, which manifests a better performance due to higher frequency reuse. The number of clusters is pre-defined in this work to satisfy the SIC constraint before the UC is conducted. Due to the limitations of having a fixed number of users per cluster and a fixed number of clusters per network, NOMA users are occasionally forced to form clusters with unfavorable users, which may lead to throughput degradation. In order to fully exploit the asymmetrical channel diversity among the NOMA users for UC, the above-mentioned limitations are omitted in [15], which permits users to dynamically form any clusters with any users with the sole objective to maximize the sum throughput, regardless of the number of users per cluster and the number of clusters per network. The authors in [15] analyzed the theoretical performance upper bound of a NOMA system by incorporating a brute-force search method (BF-S) in the UC. The BF-S approach searches all possible cluster formations and produces the optimal clustering output together with the optimal powers, which results in the highest throughput. Due to this exhaustive search, the BF-S-based UC incurs a prohibitive complexity, which is impractical for implementation in reality. In [16], the same UC problem was solved by employing particle swarm optimization (PSO) for UC in a NOMA system. The work has shown that the fast converging PSO-based UC algorithm can tremendously reduce the computational complexity, attaining a sub-optimal throughput performance. In the proposed PSO-based UC, the main issue encountered is the early convergence of the particles, causing the search space to always get trapped in a local minimum, particularly in a large NOMA network. To reduce the performance loss due to the sub-optimality in PSO-based UC optimization, a more scalable and intelligent UC scheme is required to cope with the challenging 5G environments. The existence of the BF-S-based UC has shed some light on the application of machine learning algorithms in UC problems where the UC datasets of various NOMA scenarios can be generated using the BF-S method, which can be used to train the machine learning models for UC purposes.