# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 5 Results and Discussion  
The paper evaluates the performance of the proposed model with a power-control mechanism. It enables the selection of optimal power coefficients such that the system performance is improved. The network scenario is simulated in MATLAB, taking \(10^{4}\) iterations per simulation setup. The network coverage area of \(1\) Km \(\times\)\(1\) Km is considered in, which 100-400 access points (APs) are deployed at random. The user distribution is taken to be uniform within the coverage area. The system is simulated for different parametric variations with different power optimization mechanisms, precoding schemes, spatial correlations, and CSI availabilities. A comparison of the two proposed algorithms based on minimum distance and maximum channel gain is also carried out. The results are presented and explained in this section. Important parameters for simulation are considered in Table 1.  
The communication framework proposed in the paper is first evaluated for different deployments of APs in the coverage area. The APs are densely deployed in the given area to provide extended coverage to the users. It has been shown in Figure 3 that more densely deployed single-antenna APs see a drop in spectral efficiency for all user locations, except in the lower region. With more number of antennas at the APs, the spectral efficiency improves even if the deployment of APs is less dense. The performance evaluation is carried out using the LMMSE precoding scheme, which leads to better interference suppression with \(M=4\), resulting in improved SE performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(U\) & 40 & \(l_{p}\) & 10 \\ \(A\) & 100 & \(l_{c}\) & 200 \\ \(N\) & 4 & \(p_{u}\) & 100 mW \\ \(B\) & 20 MHz & \(\sigma^{2}\) & \(-94\) dBm \\ \(T_{c}\) & 2 ms & \(\rho_{max}\) & 200 mW \\ \(B_{c}\) & 100 kHz & \(H^{\prime}\) & 10 m \\ \(\alpha\) & 3.76 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Important parameters considered in the simulation setup. Figure 3: Comparison of system spectral efficiency for different AP configurations with LMMSE precoding. The distributed network operation with the MR and LPMMSE precoding methods is shown in Figure 4. The effect of spatial correlation on the performance of the network is evaluated. With spatially correlated Rayleigh fading channels, the SE degrades with both MR and LPMMSE precoding schemes. Since all the signal processing is carried out locally at the AP, this network operation could not exploit the spatial correlation. Additionally, the system with highly correlated channels has less average spectral efficiency (SE), as compared to the system with lower spatial correlation. The availability of knowledge of the channel state information (CSI) plays an important role in determining the performance of any wireless network. This is depicted in Figure 5 for the centralized and distributed precoding schemes, namely, PMMSE, PRZF, LPMMSE, and MR. The achieved SE by 95% of the users in the network area is plotted for the different precoding schemes under two cases. In the first case, it is assumed that users have access to the perfect channel state information (PCSI), while the second case considers channel estimates for data detection, that is, imperfect CSI (ICSI). The maximum value of 5.3462 bits/s/Hz is achieved with PMMSE precoding with PCSI, while the least SE of 1.1745 bits/s/Hz is achieved with MR precoding with the IMCSI case. There is an improvement of 4.1% in the achieved SE with PMMSE precoding with PCSI, compared to the ICSI case. The performance of the system with power optimization mechanisms is evaluated further. Power optimization refers to the selection of optimum power with which a particular AP transmits to a particular user such that the system efficiency is increased. Three power-allocation mechanisms are considered in this paper, namely, equal power allocation, fractional power allocation, and sum spectral efficiency maximization power allocation.