# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 5 AI-Based Multi-Objective System Optimization  
Next, we implemented two AI-based multi-objective optimization techniques, namely particle swarm optimization (PSO) and genetic algorithm (GA), in order to balance out the trade-offs between the attainable throughout and associated total power consumption. Figure 8 illustrates the entire optimization process, starting with obtaining the simulation results discussed in the previous sections. Initially, 80% of the obtained simulation results' data points were used to train a random Forest Regression model, while the remaining 20% was used for validation purposes. In random forest regression, the R-squared parameter is a hyperparameter which indicates how well the model's predictions match the actual data. As the number of estimators (trees) in the forest increases, the R-squared value typically improves because each additional tree helps to reduce variance and capture more of the underlying data structure. This leads to better model performance and a higher R\({}^{2}\) value, which signifies a better fit. However, beyond a certain point, increasing the number of estimators (trees) can result in overfitting. That said, it was found that a number of estimators of 46 achieved the maximum prediction accuracy of 97.71%, as depicted in Figure 9a. Thereafter, the trained model was utilized as an optimization function in the multi-objective optimization investigation. We adopted three objective functions, namely maximizing the data rate, minimizing the power consumption, and hence maximizing the power efficiency (defined as the attainable data rate per Watt), all as functions of the number of system users. In this part of the study, we used Python version 3.11.9 as the primary programming language due to its extensive libraries and ease of use for machine learning tasks. Specifically, we utilized the scikit-learn library, which provides a comprehensive suite of tools for data analysis and machine learning. The random forest regression model was implemented using the RandomForestRegressor class from scikit-learn. The DEAP library was utilized for the GA (Non-Dominated Sorting Genetic Algorithm III (NSGA-III)) and PSO for the optimization algorithms. The computational environment was configured on a system equipped with a 5.2 GHz-clocked Intel i9-10850K CPU with 64 GB of RAM. Table 3 shows the different hyperparameters adopted for the utilized algorithms of random forest regression, PSO, and NSGA-III, along with the rationale behind each hyperparameter selection. Figure 8: Flow chart of the complete AI-based multi-objective optimization process. With that said, both AI optimization techniques (i.e., GA and PSO) resulted in the same Pareto frontier consisting of a set of Pareto optimality points, defined as the set of points where no performance criterion can objectively be made better off. In other words, for each point outside of the Pareto frontier, there exists at least one point on the Pareto frontier which is objectively better in at least one performance criterion. Ultimately, the Pareto frontier serves as a guide to which points are viable to consider as a potential optimal point. As such, Figure 9b shows the obtained Pareto frontiers for both B-SINR and LAUA alongside our proposed SDR-LTE-LAA approach, in terms of total power consumption and data rate objective functions, for numbers of users between 10 and 80. It is worth mentioning here that the inverse of the data rate was utilized here, since our objective was to maximize it rather than minimize it. Furthermore, the associated power efficiency at each point along the Pareto curve is color-coded according to the depicted colormap. At first glance, Figure 9b clearly showcases the drastic advantages introduced by the SDR-LTE-LAA approach. Not only did it yield the highest attainable data rate and lowest power consumption, but it also achieved a significantly higher power efficiency (almost twofold higher).