# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 4 Simulation Results and Discussion  
This section presents and explains the obtained results along with a comprehensive discussion about our findings. Furthermore, we present a comparison between our proposed strategy with the best SINR user association strategy [55], termed B-SINR, and the load-aware user association strategy, termed LAUA in our paper [56]. The results of this part of the study were obtained by utilizing both IBM ILOG CPLEX Optimization Studio and MATLAB R 2016b. Our simulations were executed on a high-performance PC equipped with an Intel Core i7 processor clocked at 3.8 GHz with 32 GB of RAM. The time it took to run the proposed algorithms to find the optimal solution was 95 s. This was a relatively acceptable delay which allowed the proposed algorithms to be utilized in real-life networks. The simulations were rigorously iterated 50 times to ensure robustness, with the subsequent average values computed and consolidated to form the foundation of our numerical results. The best SINR strategy associates users with the base station which has the highest SINR. However, the load-aware user association strategy associates users with BSs based on the BSs' traffic loads. Leveraging SDR, the cognitive radio technique empowers devices to dynamically explore licensed bands, identifying unutilized spectra which can be repurposed. These valuable data are then relayed to a central control server for further orchestration. Once received, and using the proposed algorithms, the control server orchestrates user association to the most optimal base station, with compensation involved. Notably, in this model, if the primary user (i.e., operator customer) resumes activity, then the control server proactively requests secondary users to gracefully exit the allocated band. This strategy fosters a mutually beneficial environment wherein dormant base stations, irrespective of ownership, can unlock additional value by catering to secondary users from diverse operators. Figure 3 displays a hypothetical deployment of the SDR-based system in the business district area of Perth, which is a major city in the state of Western Australia. In order to benchmark our proposed scheme, the system model shown in Figure 3 was implemented in MATLAB R2015b. Table 1 lists the values of the used parameters in our simulations. In the simulation model, three macro base stations with a 1000 m radius [57] were implemented. All the SDR-based small-cell BSs were assumed to have circular coverage shapes and uniform distribution across the areas covered by the macro base stations. Additionally, subcarriers were randomly and uniformly distributed across the BSs. In our simulation, because the size of the users' data followed a general distribution, the average data size was set to be 200 Kbits [24]. Furthermore, the arrival of users was assumed to follow the Poisson process. Figure 4a presents a comparison between our proposed approach (SDR-LTE-LAA), the best SINR, and the LAUA approach in terms of the total network data rate under a different number of users. Based on the results shown in Figure 4a, it is evident that our proposed SDR-LTE-LAA approach could deliver a considerably higher data rate than the other two approaches. Due to the utilization of LTE-LAA and SDR technology, our proposed approach tended to assign users continuously to the BSs which had more subcarriers and were closer to the users, irrespective of their operator, to provide high data rates and less