## 1 Introduction
This integration facilitates high system capacity, enhances spectral efficiency, supports massive connectivity and reduces latency in the network [8]. This technique concurrently assigns non-orthogonal resources amongst the multiple users whilst having some interference on the receiver side. In contrast, the traditional OMA techniques assign orthogonal resources solely to individual users, thus incurring challenges in achieving the required system capacity and spectral efficiency in 5G. Figure 1 depicts the pictorial representation of NOMA when compared to OMA. Considering each user a sensor or an actuator, the power with respect to the resource graph is plotted in order to understand how the resources are allocated to the users in both cases. Figure 1 represents the OMA scheme--how the users are orthogonal to each other and have a dedicated/assigned RB for each user (\(U_{1}\), \(U_{2}\), \(\ldots\), \(U_{N}\)). On the other hand, Figure 1 represents the NOMA scheme--how multiple users share a single RB, as the available bandwidth is not split across the users and assigns a single frequency channel to multiple users. Hence, the bandwidth wastage is minimised and the system throughput is increased since the entire frequency spectrum is accessible to each user for transmission. Consequently, it can be inferred that the NOMA environment is crucial and allows a dense deployment of sensors and actuators from users to share the assigned single RB, thus increasing the spectral efficiency and capacity compared to traditional OMA. Moreover, NOMA offers improved user fairness and QoS by having flexibility in Power Allocation (PA) and allowing users to be prioritised for stricter latency requirements by allocating them more power for faster data transmission. There are fewer waiting times to access the resources as users are sharing the resources concurrently, so this leads to overall reduced latency in data transmission. Finally, NOMA can effectively support heterogeneous networks with diverse user requirements and traffic characteristics. These benefits suit TI applications [1], such as enhanced mobile broadband, massive connectivity and latency-sensitive applications. In addition, NOMA techniques can be categorised into Power-Domain NOMA (PD-NOMA) and Code-Domain NOMA (CD-NOMA). In PD-NOMA, multiple users share the same RB. However, each user (whether a sensor or an actuator) is identified or allocated with varying power levels (coefficients) for transmission according to the channel conditions and distance from the Base Station (BS). Users with high channel gain (near BS) are allocated low power levels, whereas users with low channel gain (far from BS) are allocated high power levels. It utilises superposition coding (multiplexing) at the BS and performs Successive Interference Cancellation (SIC) at the receiver to detect and extract the users' original data signals, and vice versa. In CD-NOMA, multiple users share the same RB and are assigned distinct non-orthogonal codes, such as Low-Density Spreading (LDS), Sparse Code Multiple Access (SCMA) and Multi-User Shared Access (MUSA) [9]. Increasing the number of users in CD-NOMA requires sophisticated algorithms for multi-user reception when non-orthogonal codes are adopted. Furthermore, in PD-NOMA, both Uplink (UL) and Downlink (DL) transmissions play crucial roles in facilitating seamless message exchange between the user (whether a sensor or an actuator) and the BS. Considering UL PD-NOMA, the simultaneous transmission happens from user to BS using the same RB and is distinguished by varying power levels. Conversely, for DL PD-NOMA, the simultaneous transmission happens from BS to user, where BS assigns the proportion of power levels to multiple users based on the channel conditions and distance between BS and user. Therefore, these PD-NOMA concepts enhance system capacity and optimise spectral efficiency, thus accommodating more sensors and actuators and reducing congestion in the TI communication infrastructure.