# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 5 AI-Based Multi-Objective System Optimization
This is evident by following any horizontal line across the three curves indicating the attainable data rate for any given power consumption level. In other words, our SDR-LTE-LAA approach provides yet another vital feature, which is efficient green scalability, as the data rate and expected number of users grew per the requirements and expectations of next-generation communication systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Hyperparameter** & **Value** & **Rationale** \\ \hline Random Forest & Number of trees & 46 & Results in the maximum R-squared value \\  & Maximum depth & None & Allows trees to expand fully to capture more \\  & Minimum samples & 2 & Default value to ensure all potential splits \\  & to split & 2 & are considered \\  & Criterion & MSE & Mean squared error (MSE) is used as a standard \\  & & & measure for regression problems \\ \hline NSGA-III & Population size & 100 & Provides a diverse set of solutions to explore the \\  & Crossover rate & 0.9 & \begin{tabular}{c} To encourage more genetic mixing, which is \\ beneficial for diversity \\ \end{tabular} \\  & Mutation rate & 0.01 & \begin{tabular}{c} To maintain diversity and avoid \\ premature convergence \\ \end{tabular} \\  & \begin{tabular}{c} Number of \\ generations \\ Selection method \\ \end{tabular} & 50 & \begin{tabular}{c} To ensure sufficient iterations for the population to \\ evolve and converge toward optimal Pareto fronts \\ \end{tabular} \\  & \begin{tabular}{c} Crowded tournament \\ selection \\ \end{tabular} & \begin{tabular}{c} To ensure diversity among solutions by considering \\ both rank and crowding distance \\ \end{tabular} \\ \hline PSO & Swarm size & 50 & Provides a sufficient number of particles to explore \\  & & & the search space effectively \\  & Social coefficient & 2.0 & \begin{tabular}{c} Encourages particles to move toward the best \\ positions found by the swarm \\ \end{tabular} \\  & \begin{tabular}{c} Cognitive \\ coefficient \\ \end{tabular} & 2.0 & \
\begin{tabular}{c} Ensures particles are influenced by their own \\ best-known positions \\ \end{tabular} \\  & Inertia weight & 0.7 &
\begin{tabular}{c} Balances exploration and exploitation in the \\ search process \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Adopted hyperparameters for each of the utilized AI algorithms, along with the rationale behind the selection of each of them. Thereafter, to pick the optimal point for each of the three approaches, we implemented the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making (MCDM) algorithm developed by [PERSON] and [PERSON] [60; 61] where each point is ranked according to its similarity to the ideal solution. The TOPSIS optimal point for each approach is indicated by the green stars in Figure 9b, which for the SDR-LTE-LAA approach was shown to have 55 users with a total power consumption of 518 W and data rate of 432 Mbps, resulting in a power efficiency of 0.83 Mbit/J. One the other hand, the corresponding values for the LAUA (B-SINR) approach were 656 (704) W, 338 (316) Mbps, and 0.52 (0.45) Mbit/J, realized with 50 (62) users, respectively. Evidently, our SDR-LTE-LAA approach showcased superior performance metrics across the board, with a 100 Mbps higher data rate compared with the other approaches and 150-185 W (20-26%) less power consumption for the respective TOPSIS optimal point for each case.