# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
###### Abstract  
The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed SCs and the interference that arise between them. An approach to ameliorate the PE is proposed by switching off the redundant SCs using machine learning (ML) techniques while sustaining the quality of service (QoS) for each user. In this paper, a linearly increasing inertia weight-binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching is proposed to minimize the power consumption of the network. Moreover, a soft frequency reuse (SFR) algorithm is proposed using classification trees (CTs) to alleviate the interference and elevate the system throughput. The results show that the proposed algorithms outperform the other conventional algorithms, as they reduce the power consumption of the network and the interference among the SCs, ameliorating the total throughput and the PE of the system.  
5G HetNets; BPSO; classification trees (CTs); soft frequency reuse (SFR); small cells (SCs) Article Binary PSO with Classification Trees Algorithm for Power Efficiency in 5G  
Working on the IEEE Transactions on Circuits and Systems (IWT), 2015 IEEE International Conference on, 2015 IEEE,  
The main objective of our paper is to propose a new approach for the irregular nature of the 5G HetNets that merge the usage of both binary particle swarm optimization (BPSO) algorithm with linear increasing inertia weight (IW) and soft frequency reuse (SFR) to maximize the power efficiency (PE) of the SCs and minimize the number of active SCs while guaranteeing the QoS for the UEs. In SFR, every SC is split into center and edge regions where one of the unutilized sub-bands by the edge regions of the adjoining SCs is allocated to the edge region of the SC, while the remaining sub-bands are utilized in the center region of the SC with reduced transmission power to diminish the interference to the adjoining SCs. Unlike the prior works [9; 13; 14] that will be discussed later in Section 2, the proposed algorithm utilizes the linear increasing IW-BPSO algorithm for selecting the SCs to be switched on/off, and we propose the SFR utilizes classification trees (CTs) to enhance the network PE, taking into consideration the irregular nature of the 5G HetNets. The on/off switching of SCs using BPSO algorithm is carried out first; then the SFR is applied for sub-band allocation to minimize the number of operations required for allocating the sub-bands to the SCs.  
The main contributions of this paper can be summarized as follow:  
1. Propose an algorithm for irregular 5G HetNets based on BPSO algorithm for SC on/off switching to ameliorate PE of the system, and, using a linearly increasing IW approach where the IW is linearly increasing in each iteration, to enhance the convergence of the BPSO algorithm.
2. Propose a novel frequency allocation algorithm for SFR based on the CTs as it is simple and accurate machine learning (ML) technique to mitigate the interference among the irregularly shaped SCs.  
Results demonstrate that the proposed algorithms have superior performance over the conventional algorithms (always on, random 10%, and BPSO only), as it has higher total system throughput and PE, and lower system power consumption and outage probability.  
The remainder of the paper is organized as follows: the literature review is presented in Section 2. Section 3 demonstrates the system model, and Section 4 explains the proposed  
Figure 1: General representation of HetNet scenario with densely deployed small cells.  
algorithms. Then, Section 5 shows the simulation results. Eventually, Section 6 concludes the paper.