## 5 Results  
This section lists simulation results to numerically validate our algorithm and compare the results obtained using other well-established algorithms with the same conditions. We carried out the simulation using MATLAB to create a complex indoor environment with frequency-selective fading.The datasets are provided by [PERSON] [23]. It captures various practical aspects of wave propagation, such as frequency-selective fading, orthogonal frequency-division multiplexing signaling, and mutual coupling between closely spaced elements. Our setup involved a wireless base station serving multiple users, with an IRS containing 4096 controllable elements placed on a wall to improve signal strength. These IRS elements had two tunable impedance states to reflect radio waves. We aimed to optimize data rates for 50 user locations by identifying the best IRS configurations. The dataset provided noisy received signals corresponding to various pilot signals and IRS configurations. We processed these signals to find the optimal setup, and due to the impracticality of exhaustively searching all possible configurations (\(2^{4096}\)), we employed efficient selection algorithms. We faced challenges in channel estimation, with more unknowns than observations, which required structured dimension reduction techniques. We used two evolutionary algorithms, and also compared these with algorithms such as genetic algorithms, to navigate the ample configuration space effectively. Our work demonstrated the necessity of interdisciplinary expertise in electromagnetics, communication, and signal processing to develop practical IRS configurations. As mentioned in the previous section, we aimed to increase the overall rate across all subcarriers (14) for both LOS and NLOS users.The 64 \(\times\) 64 spatial reflecting surface, in order to beam form the signal to particular user, has an horizontal and vertical indexed arrangement, as retrieved from dataset1, and its nature is shown in Figure 2.  
The columns show that the turning of the diodes On or Off is same throughout the vertical direction and minimally varies as we progress in the horizontal direction of the cell arrangement.Hence, the impedance applied in vertical direction arrangement of unit cell is supposed to be constant for achieving the proper beamforming of the signal and achieving a high data rate. The mutual coupling induced at the center of the 64 \(\times\) 64 cell's IRS pattern is measured, which is plotted in Figure 3. Thus, a tightly packed element's cell results in significant coupling losses, and the cell under observation is more impacted by neighboring cells. The coupling capacitance [33] was measured with the help of Equation (15). \[C_{n}=\sum_{i=1}^{N}\widetilde{C}_{i,\theta}\frac{100^{-d_{n,i/\lambda}}}{\sum _{j=1}^{N}100^{-d_{n,j}/\lambda}} \tag{15}\]  
where \(d_{n,i}=\|\mathbf{u}_{n}-\mathbf{u}_{i}\|\) is the distance measured between \(n\)th cell and \(i\)th cell. Figures 4 and 5 represent the element unit cell's phase plot and amplitude response plot. The waveform clearly shows that at 5 GHz frequency, the phase difference of 180\({}^{\circ}\) is applied at the incident waveform by the unit cell, which will help the incident EM wave to reflect in the desired receiver direction. The amplitude loss of 3% is observed in the diode Off state and by 7% in the On state. Figure 2: Reflecting surface horizontal and vertical index 64 \(\times\) 64 cell estimation. The pathloss [19]\(\beta_{IRS}\) is measured by Equation (16) and is plotted in Figure 6 for different arrangements of IRS elements. In pathloss calculations, the incident angle \(\Theta_{i}=30^{\circ}\), the desired angle \(\Theta_{r}=60^{\circ}\), where the antenna gains are maintained at \(\mathrm{Gt}=\mathrm{Gr}=5\) dB, the distance \(\mathrm{di}=100\) m, and the receivers are at 40 m.  
Figure 4: Phase plot for the cell arrangement. Figure 5: Amplitude plot for the cell arrangement. Figure 3: Mutual coupling plot at the center of the cell arrangement.