# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 6 Conclusions  
In this work, we introduced a flexible software-defined radio-based HetNet architecture which can promptly adjust to changing network requirements. The proposed framework combines LTE-LAA with SDR technology to optimize the network throughput while minimizing the overall power consumption within HetNets. We approached the resource management challenge by framing it as a constrained optimization problem, and our solution significantly outperformed existing methods. Our approach boasts a remarkable 17% reduction in energy consumption compared with current practices. Furthermore, in terms of the network data rate and utilization of the licensed spectrum, our solution offers an impressive 31% increase in data rate and nearly 22% utilization of the licensed spectrum courtesy of LTE-LAA technology adoption. We proved that the optimization problem was an NP-hard problem. For an NP-hard problem, time complexity may become prohibitively high when the number of parameters becomes high in the model. Considering the time complexity of solving an NP-hard problem and the desire to obtain a real-time solution, we also introduced a throughput- and energy-aware heuristic solution. The proposed heuristic was found to perform considerably better than other approaches. In this study, we adopted random forest regression, PSO, and GA, due to their robustness, ability to handle large datasets with high dimensionality, and effectiveness in capturing complex, nonlinear relationships within the data (random forest regression). Furthermore, random forest regression's ensemble nature helps reduce overfitting and provides a reliable performance benchmark for our analysis. This was demonstrated by the  
Figure 9: (**a**) Attained R-squared value based on the number of estimators used to train the random forest model. (**b**) The 3D design space, showing the three objective functions of the system depicting the Pareto frontier of each of the three approaches, alongside the TOPSIS optimal point, denoted by the green stars. Note the direction of the \(X\) axis. The power efficiency is color-coded at each point for each case. The dashed arrow indicates the direction of an increasing number of users from 10 to 80.  
high agreement between the model's prediction and the actual data, as evident by the 97% R-squared value. However, future work could explore alternative optimization approaches to further validate and potentially enhance our findings. Dynamic programming (DP) could be investigated for its strength in solving problems with an ideal substructure, despite its computational limitations in high-dimensional spaces. Additionally, deep reinforcement learning (DRL) offers a promising adaptive and learning-based framework which may provide novel insights and performance improvements, though it requires extensive training data and computational resources. Future research may consider these methodologies to compare their efficacy and robustness against the adopted methodology used in this study. Future research efforts could explore potentially incorporating network traffic analysis, latency measurements, and scalability considerations in a cross-layer architecture. In addition, the effect of the SDN controller placement (typically centralized for accessibility to all base stations in the network to track their traffic) can be investigated in detail as its own optimization parameter among other HetNet variables. Conceptualization, H.K. ; methodology, H.K. and E.A. ; formal analysis, H.K. ; investigation, H.K. and E.A. ; data curation, H.K. ; writing--original draft preparation, H.K. and E.A. ; writing--review and editing, E.A. ; visualization, H.K. and E.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: