Osama Mayada, El Ramly Salwa, Abdelhamid Bassant
Electronics and Communications Department, Faculty of Engineering Science and Arts, Misr International University, Cairo 11828, Egypt.
Electronics and Communications Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt.
Sensors (Basel). 2022 Nov 7;22(21):8570. doi: 10.3390/s22218570.
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异构网络（HetNets）中小小区（SCs）的密集部署满足了大量连接和更高数据速率的需求。不幸的是，由于密集部署的小小区功耗增加以及它们之间产生的干扰，网络的功率效率（PE）降低了。提出了一种通过使用机器学习（ML）技术关闭冗余小小区来改善功率效率的方法，同时为每个用户维持服务质量（QoS）。本文提出了一种用于小小区开/关切换的线性增加惯性权重-二进制粒子群优化（IW-BPSO）算法，以最小化网络的功耗。此外，提出了一种使用分类树（CTs）的软频率复用（SFR）算法来减轻干扰并提高系统吞吐量。结果表明，所提出的算法优于其他传统算法，因为它们降低了网络的功耗和小小区之间的干扰，改善了系统的总吞吐量和功率效率。
