## 4 Performance Evaluation
### Simulation Results and Discussion
#### 4.2.6 Performance Comparison and Analysis of 4 \(\times\) 4 MIMO Scenario in NOMA  
In our simulation, the performance comparison of 4 \(\times\) 4 MIMO-NOMA with SISO-NOMA is conducted and observed in terms of the achievable sum rate and latency with a fixed \(\eta=4\) and 10 users, as previously mentioned in Section 3.5. The simulation scenario considers the user's distances to be between 1000 m and 200 m from a BS, with User 1 being farthest and User 10 being closest to the BS. From User 1 to User 10, the distances from the BS are 1000, 900, 800, 700, 600, 500, 400, 300, 250 and 200 metres. Five user clusters are formed, with 2 users in each cluster with a near-near and far-far clustering approach. The achievable sum rate and latency comparison are analysed in the following cases: \(a\) and \(b\).  
Case _(a)_: Performance comparison and analysis of achievable sum rate between 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA.  
The achievable sum rate comparison is observed for 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA in Figure 11 when plotted against transmitted power. This figure represents an increase in the achievable sum rate as the transmit power increases for both cases, 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA, thus improving SNR for higher transmitted power. It can be clearly seen that 4 \(\times\) 4 MIMO-NOMA outperforms SISO-NOMA as transmitted power increases. This is because of the exploiting of spatial diversity and multiplexing, thus allowing multiple data streams to be transmitted from the BS to multiple users simultaneously in 4 \(\times\) 4 MIMO-NOMA. Moreover, the incorporation of ZF-BF further improves the performance of 4 \(\times\) 4 MIMO-NOMA by reducing the spatial inter-user and inter-cluster interference. In contrast, a round-robin scheduling process promotes fair user access to the RB as it cycles through the clusters of users and allocates time slots sequentially, which does not prevent any clusters from having access to RB and does serve each cluster in each round.  
Figure 10: Latency comparison between fair and fixed PAs in SISO-NOMA.  
Case (_b_): Performance comparison and analysis of latency between 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA.  
Figure 12 represents the latency plot of 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA against the increasing transmitted power. It can be observed that 4 \(\times\) 4 MIMO-NOMA incurs less latency than SISO-NOMA. This is because of effectively nullifying interference in ZF-BF; thus, clusters of users do not have to wait for a clear transmission slot from the BS and are served simultaneously. Also, it helps in interference-free streams for each cluster of users, thus reducing the need for retransmission and error correction and contributing to overall lower latency. However, the complexity of designing a beamforming matrix for many antennas or users may potentially increase the overall latency. However, with careful beamforming matrix design, reduced interference and parallel data transmissions offset latency. On the other hand, all clusters of users are served sequentially in a round-robin scheduling process, thus reducing the waiting time of clusters of users, promoting user fairness and overall latency. Also, this scheduling process reduces the queuing delay as it cycles through the clusters of users. Without any scheduling process, the clusters of users may experience delays or have to wait longer for their turn for transmission, thus making higher overall latency and lesser system responsiveness and avoiding constant allocation of RB to high-priority users or users having channel conditions.  
Figure 11: Achievable sum rate comparison between 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA.  
Figure 12: Latency comparison between 4 \(\times\) 4 MIMO-NOMA and SISO-NOMA.