## 3 Simulation Results
### Synchronization Effects on Modulation Impairments  
While there are several impairments to observe within this configuration, here we focus on LO frequency offset, LO phase offset, and sampling time offset. When the frequencies of local oscillators at the transmitter and the receiver do not match, then modulation accuracy is degraded. Figure 12 presents the constellations for several frequency offset values in terms of parts per million (ppm) and corresponding EVM measurements, where only the last case (d) offers satisfying accuracy (red EVM) for this parameter setup (\(p,\ \alpha,\ L\)). It is important to note that the EVM measurement associates measured values inside each decision region and does not directly show coding performance.  
Figure 14: NOMA constellations and EVM for different sampling offsets: (**a**) \(L=1\), (**b**) \(L=3\), (**c**) \(L=4\), (**d**) \(L=6\), and (**e**) corresponding EVM.  
Figure 13: NOMA constellations and EVM for different LO phase offset: (**a**) 0, (**b**) \(\pi/16\), (**c**) \(\pi/4\), (**d**) \(3\pi/4\), and (**e**) corresponding EVM.  
Figure 13 shows measurements taken for a second test scenario. In this scenario, frequency synchronization is achieved, but the receiver's local oscillators suffer from a slight phase shift. This phase shift is simulated with phase granularity of \(2\pi/L\), so with higher \(L\) one can achieve more accurate phase synchronization, which is especially important for high modulation orders. In this case, EVM diagrams show certain spikes, pointing to certain symbol errors. Further analysis of EVM on a symbol basis could show which symbols are most affected. A third simulation test is reported in Figure 14. In this test, full LO synchronization is assumed, while the sampling offset is varied. It is obvious from the figure that with offset of 4 samples (out of 32 that represent a single symbol), the lowest EVM (below 2%) and better accuracy was achieved.  
Promising applications for the simulation model presented in this work are found in the context of model-based neural networks: Recent research works stated that utilizing deep neural networks for solving complex communication scenarios requires massive data sets for training, which is a major drawback [18; 19]. Although these networks require substantial computational resources, current research of deep unfolding technology in NOMA mobile ad hoc networks shows encouraging advances [20]. An interesting approach here is to generate synthetic data for training processes of neural networks, [21]. This approach can greatly benefit from our proposed intuitive symbolic framework.  
To illustrate this point, Figure 15 presents a neural network model that maps various parameters and impairments in the NOMA transmission chain, such as oversampling rate, LO phase offset, and sampling misalignment, to optimal compensation parameters like power allocation, synchronization tuning, and pulse shaping. Experimental results from our model can be used to guide real-time reconfiguration of SDR-based NOMA systems by predicting and adjusting critical physical-layer parameters for improved link performance under dynamic channel conditions.