## 5 Results
\[\beta_{\text{IRS}}(r,d_{i},\theta_{r})=\frac{G_{t}G_{r}}{(4\pi)^{2}}\bigg{(}\frac{ ab}{d_{i}r}\bigg{)}^{2}\cos^{2}(\theta_{i}) \tag{16}\]  
There are two stages in maximizing the rate equation. Stage 1 involves the initialization algorithm, Algorithm 1, and further optimization is carried out by Algorithm 2. So, along with this proposed technique, we have presented the rates for the genetic and gradient descent algorithms. The optimization problem is non-convex, where each IRS element is in a binary discrete state \(-1\),\(+1\). We must relax the discrete space to continuous space [\(-1\),\(+1\)] to facilitate the gradient search. Further, we must meet this bounded continuous space to unbounded continuous search space over an entire real number line. So, the data rate function R mentioned in Equation (14) is used to define the cost function of gradient descent, as in Equation (17). \[C(x)=-R(\tanh(x)) \tag{17}\]  
The binary nature of IRS fits well with the genetic algorithm. It also takes less time than gradient descent. We have to enforce the periodicity vertically for the final configuration for uneven configurations. This results in the overall highest data rate. So, using the genetic algorithm alone is the fastest way to achieve data rates up to 111.683 Mbps for LOS and 51.389 Mbps for NLOS. Figure 7 gives the plot for cost metric convergence, where the genetic algorithm is used together with gradient descent. Figure 6: The path loss of the reflected path. Figure 3.0 The convergence of the algorithm. The genetic algorithm outputs a warm start for the gradient descent algorithm. So, running the genetic algorithm and gradient descent alternatively results in accurate, precise, high data rates, with 99.15% accuracy. In configuring the IRS for wireless communications, the algorithm operates with varying time requirements. Achieving the best possible configuration can take up to 6 min, reflecting the complexity and thoroughness of the calculation process. Alternatively, a faster configuration, completed in approximately 0.3 s, indicates a compromise in computational efficiency while still aiming to find a feasible solution. The selection between these timings depends on the need for precision versus expedience in achieving operational readiness for the IRS setup. Figure 8 corresponds to the received signal power across all subcarriers for different users and IRS configurations. Figure 8a,b corresponds to the same LOS user, whereas Figure 8c,d relates to the same NLOS user. An IRS configuration is chosen without optimization during pilot transmission, keeping the diode condition in the Off state. In Figure 8a, The graph shows the received power fluctuating between approximately \(-115\) dBW and \(-155\) dBW across the subcarrier index. In Figure 8c, The graph shows the received power fluctuating between approximately \(-110\) dBW and \(-145\) dBW across the subcarrier index. These plots illustrate the received power levels without optimizing the IRS, providing a baseline for LOS and NLOS users. The rates are calculated using B = 10 MHz, P = 1 W, M = 20 and K = 500. The estimated variance of \(\mathcal{N}_{\mathrm{C}}\) is 3.1614 \(\times 10^{-20}\). The rates for the pilot transmission and our optimized configuration, in comparison with other methods in the literature [34], all computed using channel estimation, are shown in Figure 3.1 The work by our team, named Meta-Darpan as mentioned in [23], reached 10 th  
Figure 3.2 Received signal power across all subcarriers. (**a**) LOS user with pilot transmission, (**b**) LOS user with optimized IRS configuration, (**c**) NLOS user with pilot transmission, (**d**) NLOS user with optimized IRS configuration. position with 117.58 Mbits/s in the average metric scale. The algorithm is further improved in Algorithms 1 and 2 to achieve the proposed metric for the LOS and NLOS scenarios, as given in Figure 3.3  
The rates are increased by approximately six and three times for LOS and NLOS users, respectively.