## 5 Experimental Results  
We simulated the network in MATLAB and solved the problem in Equation (8a) using OAA. The value of \(P_{BS}\) is 20 dBW. \(P_{UE}\) and \(P_{RIS}\) is 10 dBm. The minimum data rate \((R_{min})\) is 0.1 bits\(/\mathrm{s}/\mathrm{Hz}\). The maximum number of users \((max_{UE})\) is 300 whereas the minimum number of users \((min_{UE})\) is 2. The simulation parameters for the network are summarized in Table 2.  
The performance of the proposed algorithm is compared with the plethora of algorithms such as particle swarm optimization (PSO) [29], firefly algorithm (FA) [30], artificial bee colony (ABC) [31], cuckoo search (CS) [32], teaching learning-based optimization (TLBO) [33], basic open-source nonlinear mixed integer programming (BOMIN) [34], social group optimization (SGO) [35], mesh-adaptive-direct-search-algorithm (NOMAD) [36] and genetic algorithm (GA) [37] to compare and validate the results of the proposed algorithm. Figure 5 shows the complete performance analysis of the proposed algorithm. The throughput of the network for all the algorithms is shown in Figure 5a. The highest average \(\Omega\) achieved by OAA is 90 Mbps. The proposed algorithm starts converging when the number of \(U_{E}\) is greater than 180. Figure 5a also shows that the performance of the OAA is better than other algorithms.  
The EE and the latency of the network are also evaluated using several algorithms as shown in Figure 5b. Figure 5b shows that the performance of the OAA is better than other algorithms. OAA achieves optimal \(\mathbb{EE}\) of 9 Mb\(/\)J whereas the highest \(\mathbb{EE}\) achieved by the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(P_{BS}\) & 20 & dBW \\ \hline \(P_{UE}\) & 10 & dBm \\ \hline \(P_{RIS}\) & 10 & dBm \\ \hline \(R_{min}\) & 0.1 & bits\(/\mathrm{s}/\mathrm{Hz}\) \\ \hline \(min_{UE}\) & 2 & – \\ \hline \(max_{UE}\) & 300 & – \\ \hline \(\mathbb{N}\) & 32 & – \\ \hline \(\mathbb{K}\) & 16 & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
other algorithms, particularly NOMAD, is 8.5 Mb /J. OAA shows significant improvement in \(\mathbb{EE}\). The optimal latency achieved by the OAA is 0.5 ms for 90 \(U_{E}\), whereas the minimum latency achieved by NOMAD is 1 ms. The performance of NOMAD is better than other algorithms. However, OAA outperforms NOMAD.  
We evaluated the channel efficiency (\(\mathbb{CE}\)) of the RIS channel and NLOS using OAA. Figure 5c shows the \(\mathbb{CE}\) in the percentage of OAA with \(\Omega\). The \(\mathbb{CE}\) of RIS-assisted network is 95%. However, the \(\mathbb{CE}\) of the without RIS is 85% which signifies the importance of RIS surface in the network.  
Figure 5d, shows the allocation of the RIS to the \(U_{E}\). When the number of \(U_{E}\) increases, more \(U_{E}\) are shifted on RIS to avoid blockage caused by the NLOS. The minimum number of \(U_{E}\) that successfully communicates with RIS is 10. However, when the number of users increases from 10, the \(U_{E}\) is shifted on the RIS channel because the network exceeds the mobile communication network forecast limit. The forecast is predicted using Algorithm 2. The utility improvement makes the network stable and allocates optimal resources to all \(U_{E}\)s.  
The upcoming section presents the conclusion.