## 1 Introduction
Ray tracing consumes more time at lower frequencies while effectively working at higher frequencies such as in GHz and THz communication [28]. Cooperative relaying is used to enhance the performance of far-users by using the near-users as relay nodes in the system [29]. Moreover, the power optimization problem is a crucial part of PD-NOMA to deal with. In the previously mentioned work, the power allocation has no such dedicated and optimal power allocation strategies. Most of them performed the power allocation process using classical methods such as DBPA, and some used random power factors for PD-NOMA users. The limitation of the previous methods for power allocation strategies such as in [16; 17; 18] is the user-fairness. Considering only the distance parameter is not enough for accurate power distribution among NOMA users. However, it is urgently needed to propose a power allocation strategy that is optimal and ensures user-fairness. To address this issue, we are proposing a human intuition-based fuzzy inference system approach that not only considers the distance separation of users from the gNodeB but also incorporates other parameters as well, such as signal-to-noise ratio (\(SNR\)) and foliage depth (\(d_{f}\)) during the power allocation process. The purpose of considering these multi-input parameters is to make the power allocation process accurate and optimal so that each user is served with equity and fair-play during power distribution. For the allocation of relay nodes, a fuzzy logic-based method is utilized, which effectively improves the choice of adaptive data rates and assigning power for NOMA transmission in [30]. To optimize the productivity of conventional and complex fuzzy systems, the Mamdani complex fuzzy inference system (Mamdani CFIS) was developed by [PERSON] et al. in [31]. It has been shown that the suggested Mamdani CFIS is simpler, faster, and provides a more efficient way of dealing with time information and time-periodic events than any other fuzzy IS identified in the literature to date. Similarly, the Fuzzy-System Kernel Machines,a family of machine learning techniques concerning the relationship of FIS systems and kernel machines, were introduced by [PERSON] et al in [32]. The fundamental motivation for utilizing FL to solve these problems is that they are difficult to model with mathematical equations. Instead, systems that replicate people's behaviors can easily manage them. Power control, traffic control, and channel selection are some of FL's application fields. Cellular systems and bandwidth allocation in cognitive radio networks [33; 34] are two notable areas where FL is productive. An approach to generating a fuzzy inverse matrix has been introduced in [35]. Using this method, the fuzzy system is turned into an identical structure of crisp polynomial equations. The eigenvalue approach is used to calculate the solutions to the crisp polynomial equations. Fuzzy systems have a wide range of applications [36; 37; 38; 39; 40]. In [40], a fuzzy inference system is used in the medical field for the intensive care of patients where depth of anesthesia (DOA) estimation approach is designed using a machine learning algorithm and an adaptive neuro-fuzzy model. This paper presents two different and important aspects of PD-NOMA in 5G-based micro-cells: optimal PA and cooperative relaying. We developed a fuzzy logic-based system that assigns each PD-NOMA user the optimal power levels. Furthermore, we incorporate a cooperative relaying technique into the PD-NOMA system that effectively addresses the raised issues at far-user due to intense path losses.