# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## 2 Literature Review
However, in this technique, the tendency of the particles to local search is constantly increasing. The authors in [32; 33] demonstrated that the increase in IW surpasses the decrease in IW for PSO on all their tested benchmarks. Thus, the linearly increasing IW is adopted in our paper. Various interference mitigation techniques for the modern networks are presented in the literature such as advanced multiple access techniques [34; 35] and frequency reuse (FR) [13; 36; 37; 38; 39]. FR is an auspicious approach aiming to mitigate the interference in the modern HetNets [37]. SFR is presented to minimize the interference in HetNets [13; 36; 38; 39]. The authors in [36] present SFR in HetNets, where the MC organizes the assignment of the resource plans to the SCs, and the SCs choose the resource plan. However, this algorithm cannot be implemented in the absence of the MC. A multi-level SFR (MSFR) for HetNets is demonstrated in [38], where every cell is split into three zones (central, intermediate, and edge), utilizing various frequency segments and transmission power levels. The authors in [13] proposed a novel SFR algorithm to mitigate the interference by splitting the SC to the center and edge regions. Moreover, the on/off switching of the SCs depends on their interference contribution rate (ICR) values. The authors in [39] presented an MSRR scheme where every MC is split into various circular areas, and different spectrum and power are allocated to every area. Unfortunately, in this scheme, SFR is not applied to the SCs but only the MCs. New SFR approaches are essential to alleviate the interference and improve the network throughput in 5G HetNets. Moreover, the performance of 5G HetNets can be enhanced using ML techniques [40]. Decision trees (DTs) is one of the propitious supervised learning (SL) techniques, where every training example must be fed with its label to train a learning model, then utilize this model to predict the output for any new data. The authors in [41] proposed using supervised ML to enhance both the classification of services and the distribution of network resources in 5G networks. Moreover, the results reveal that DTs and random forests are the best approaches. DTs are split to classification trees (CTs) and regression trees [40]. The authors in [42] studied several anomaly detection techniques in 5G traffic. The performance of these techniques is analyzed based on multiple factors such as the probability of identifying anomalies and the probability of detecting a false positive. The results demonstrated that the CTs technique outperforms the other techniques. The problem of monitoring and predicting the quality of experience of cellular networks is studied in [43]. The authors compared various SL techniques and trained them utilizing training data based on traffic measurements of the UEs from a field trial study. The CTs are the ultimately chosen model because of their superb prediction accuracy and their prediction speed. In our paper, CT is utilized for the first time to allocate the frequencies in the SFR 5G network. Finally, to match real-life scenarios, the irregular nature of the 5G HetNets was modeled utilizing Voronoi cells [44; 45; 46; 47]. Because of the immense deployment of SCs, Voronoi cells are considered more practical than traditional hexagonal grids [48].