DeCoNet: Density Clustering-Based Base Station Control for Energy-Efficient Cellular IoT Networks

Wonseok Lee,Howon Lee,Bang Chul Jung

doi:10.1109/access.2020.3006522

Abstract

Recently, there has been a rapid increase in the number of (small-cell) base stations (BSs) to support the massive amount of mobile data traffic and rapidly increasing number of mobile devices in beyond 5G (B5G) wireless communication systems or Internet of Things (IoT) networks. However, many of these BSs tend to waste a considerable amount of energy to support such data traffic and mobile devices. Therefore, the development of an efficient BS status control algorithm is important for realizing energy-efficient IoT networks. To reduce network energy consumption, we herein propose a density clustering-based BS control algorithm for energy-efficient IoT networks (DeCoNet). DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and OPTICS (Ordering Points To Identify the Clustering Structure) are utilized for partitioning high and low user-density regions. To find the effective number of BSs and their appropriate locations considering user-density differences, we utilize parameters obtained after applying density clustering algorithms to derive the thinning radius that is used to adjust the status of BSs in overall cellular IoT networks. Specifically, the average reachability-distance of each cluster in OPTICS and the distance between the outermost border users of each cluster in DBSCAN are used to obtain the radius of each cluster region. Through extensive computer simulations, we show that the proposed algorithms outperform the conventional algorithms in terms of average area throughput, energy efficiency, energy per information bit, and power consumption per unit area.

Highlights

Future wireless networks such as the beyond fifth generation 5G (B5G) system or Internet of Things (IoT) system aim at supporting the rapidly increasing number of mobile devices and data traffic while reducing the network energy consumption compared to the fourth generation (4G) networks [1]
DeCoNet: PROPOSED DENSITY CLUSTERING-BASED base stations (BSs) CONTROL ALGORITHM In the proposed DeCoNet algorithm, the entire network is divided into several subnetworks, and the status of each BS is determined according to the condition of each subnetwork
To consider the difference in the user density per area, we partitioned the entire network into several subnetworks

Summary

INTRODUCTION

Future wireless networks such as the beyond fifth generation 5G (B5G) system or Internet of Things (IoT) system aim at supporting the rapidly increasing number of mobile devices and data traffic while reducing the network energy consumption compared to the fourth generation (4G) networks [1]. In [19], Liang et al proposed a cluster-based energy-efficient resource allocation scheme for UDNs to reduce interference and boost energy efficiency They utilized the k-means clustering algorithm to dynamically adjust the number of BS-clusters based on the density of the BSs. [20] used a modularity-based user-centric clustering to decompose the UDNs into several sub-networks by exploiting the inherent group structure of users. [20] used a modularity-based user-centric clustering to decompose the UDNs into several sub-networks by exploiting the inherent group structure of users They did not consider the user density for adjusting the modes of BSs in the UDNs. In this paper, to further improve network energy efficiency, we propose a novel density clustering-based BS control algorithm for energy-efficient (ultra-dense) cellular IoT networks (DeCoNet). The number of users in the BS coverage area (Nu.tot ) is approximately calculated as

DENSITY-BASED CLUSTERING ALGORITHM

DeCoNet

PERFORMANCE METRICS

CONCLUSIONS