# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 3 System Model
### Error Analysis  
The error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), MALE and Mean Absolute Percentage Error (MAPE) are considered for computing the error of the developed VIM6G-IoT model. \"The MSE or Mean Squared Deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors, that is, the average squared difference between the estimated values and the actual value. MAE is simply, as the name suggests, the mean of the absolute errors. The absolute error is the absolute value of the difference between the forecasted value and the actual value. MAE tells us how big of an error we can expect from the forecast on average. The MAPE is the sum of the individual absolute forecast errors, divided by the actual values for each period. It is an accuracy measure based on the relative percentage of errors. The closer the MAPE value is to zero, the better the predictions\". The MSE of IACO work is 0.011, while ZFBF, Fixed Minimum MSE (MMSE), Uniform-Forcing Beam forming (UFBF), Joint Beam forming Design Algorithm (JBDA), ACO and IACO-C obtained relatively high errors of 0.021, 0.028, 0.015, 0.012, 0.023 and 0.052. The MAE  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constraints** & **Values** \\ \hline Number of BS antennas & \(N=32,34,40,-50,-40,-30,-20,64,66\) and 80 \\ IoT UEs & \(Q=32\); \(N=2\); \(R=1\); \(L=1\) \\ Energy-conversion efficiency & \(\hat{\theta}_{\hat{q}}=\theta_{0}=0.5\) \\ Cell radius & \(Ra=500\) m \\ Minimal needed SINR threshold & 0.1 dB \\ Noise power & \(\sigma_{n}^{2}=-50\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Constraints.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constraints** & **Values** \\ \hline Learning rate & 0.2 \\ Batch size & 90 \\ Maximum iterations & 150 \\ Evaporation rate & 0.05 \\ Population size & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key settings and parameters for baseline models.  
of IACO work is 0.086, while ZFBF, Fixed MMSE, UFBF, JBDA, ACO, ANN, KNN and IACO-C obtained relatively high errors of 0.091, 0.093, 0.093, 0.090, 0.084, 0.079, 0.076 and 0.074. This lower error attainment is due to the proposed variance-based integration scheme for the 6G-IoT system. Here, ECC evaluation is done with the proposed variance-based integration with arithmetic-mean and geometric-mean specifications. Moreover, the proposed IACO algorithm deals with the sub-problem for optimization of transmit beams that also ensure the attainment of minimal MSE. The MAPE of IACO work is 0.249, while ZFBF, Fixed MMSE, UFBF, JBDA, ACO, ANN, KNN and IACO-C obtained relatively high errors of 0.346, 0.287, 0.301, 0.354, 0.412, 0.0058, 0.0054 and 0.321. For all errors (MSE, MAE, MALE and MAPE), the adopted IACO model for VIM6G IoT acquired fewer error values over the extant ZFBF, Fixed MMSE, UFBF, JBDA, ACO, ANN, KNN and IACO-C models. Despite sharing the same RF carrier, the communication and computation signals have different performance metrics. Each IoT UE's communication signal should be isolated from the mixed signal at the BS for communication. Hence, the received quality of the communication signal, or SINR, should be high. The signals from various IoT UEs are combined at the BS for computation. Thus, the computation's output ought to have a low MSE. The error analysis using the proposed IACO with other models for VIM6G IoT is tabulated in Table 3. Further, Table 3 provides the information about the throughput and latency analysis for various models.