Optimizing the ultra-dense 5G base stations in urban outdoor areas: Coupling GIS and heuristic optimization

Highlights

• The rollout of 5G still faces challenges in constructing cellular networks.

• We coupled heuristic algorithm with GIS to maximize the service coverage of 5G base stations.

• A service coverage model is designed to spatially explicit simulate the propagation of 5G signals.

• The developed model can facilitate the rollout of 5G technology.

Abstract

Due to the high propagation loss and blockage-sensitive characteristics of millimeter waves (mmWaves), constructing fifth-generation (5G) cellular networks involves deploying ultra-dense base stations (BSs) to achieve satisfactory communication service coverage. However, ultra-densely deployed BSs are associated with extremely high construction and operation costs for 5G cellular networks. Reducing the construction cost and decreasing the energy consumption of BSs under the premise of ensuring the quality and coverage of services have become major challenges for the rollout of 5G technology. Essentially, the location optimization of 5G BSs can be regarded as a type of maximum coverage location problem (MCLP). Hence, this study coupled geographic information system (GIS) and a heuristic optimization algorithm to spatially explicit simulate the propagation of 5G signals and to optimize the service coverage of 5G BSs. The developed model was applied to search for the optimal solutions in 5G cellular network planning for an urban outdoor area in Wuhan, China. The optimal solutions and comparative experiments demonstrate that the proposed model can provide reasonable and robust results to support 5G cellular network planning. Therefore, this approach can help address the cost and energy consumption challenges faced in constructing 5G infrastructures and facilitate the rollout of 5G technology.

Introduction

To meet the rapidly growing demand for wireless communication in the coming decade, in recent years, fifth-generation (5G) communication technology has been developed. As the latest generation of cellular communication technology, 5G provides a significantly larger data capacity, a higher speed and extremely low latency in wireless data transformation compared to previous technologies. Furthermore, 5G is expected to revolutionize the development of many emerging technologies (Khan, Pi, & Rajagopal, 2012; Pi & Khan, 2011), such as the Internet of Things (IoT), self-driving cars, virtual reality (VR) and artificial intelligence (AI). Moreover, 5G is regarded as one of the most essential drivers of clean economic growth in the Industry 4.0 era (Adam, 2019). Consequently, governments around the world, especially in developed countries, have attached great importance to the development and rollout of 5G technology.

Although 5G technology has been developed and matured in laboratories for a long time, the rollout of 5G still faces some challenges (Jingyi, 2019). The first challenge is the huge capital investment required for the construction of 5G cellular networks. For instance, as Yizhong Li (the former minister of the Ministry of Industry and Information Technology of China) estimated (Jingyi, 2019), the construction of 5G cellular networks in China might last 6-7 years and cost 170-200 billion US dollars to achieve nationwide service coverage. To cover the same area as traditional cellular networks (2G, 3G, and 4G), the number of 5G base stations (BSs) could be tripled (Wang et al., 2014). Furthermore, Ge, Tu, Mao, Wang, and Han, (2016) suggested that to achieve seamless coverage services, the density of 5G BSs would reach 40-50 BSs/km². Another challenge for the rollout of 5G is posed by concerns about power consumption. In the pre-5G era, information and communication technology already consumes 10% of energy worldwide (Usama & Erol-Kantarci, 2019). The environmental and economic costs associated with the energy consumption of BSs cannot be ignored (Buzzi et al., 2016). It is estimated that the energy consumption of each 5G BS is approximately 2-3 times that of a 4G BS (I et al., 2014). Considering the ultra-dense deployment characteristics of 5G BSs, to achieve seamless service coverage in the same area, the total energy consumption of 5G BSs might be 9 times or more than that of 4G BSs.

Clearly, optimizing the deployment of 5G cellular networks will play an essential role in addressing these challenges related to the rollout of 5G (Buzzi et al., 2016) because optimization can significantly reduce the number of BSs required to cover the same area. Not only is the location optimization of 5G BSs an effective way to downscale the energy consumption of cellular networks, but it also helps reduce the construction cost of 5G cellular networks and helps improve service coverage. Although previous studies have developed many optimization models to solve the BS location optimization problems in 2G/3G/4G cellular network planning, a robust and spatially explicit optimization model that considers the propagation characteristics of 5G signals for the location optimization of 5G BSs is still lacking.

The objective of this study is to develop a location optimization model to support the planning of ultra-dense 5G BSs in urban outdoor areas and to help address the cost challenges facing 5G. In this study, we couple geographic information system (GIS) and a heuristic algorithm to search for the optimal locations of each BS in a 5G network. The spatial modelling and visualization approaches provided by GIS will be used to simulate the signal propagation and service coverage of 5G BSs in urban outdoor areas. An artificial immune system (AIS) algorithm, which is a robust heuristic optimization algorithm, will be employed to optimize the service coverage of 5G cellular networks. The AIS algorithms were developed based on the principles of natural immune systems (de Castro & Timmis, 2003), and AIS algorithms have been widely used in spatial optimization (Ma & Zhao, 2015; Zhao, Ma, Tang, & Liu, 2019). Previous studies have shown that AIS algorithms can avoid local optima and provide superior convergence characteristics for solving optimization problems (Huang, Liu, Li, Liang, & He, 2013; Shang, Jiao, Liu, & Ma, 2012).

The remainder of this paper is organized as follows: Section 2 presents a short introduction on 5G communication technology and a brief review of location optimization models for cellular network planning. The formulation and modelling of 5G BS optimization problems are defined in Section 3. Section 4 describes the location optimization algorithm based on GIS and the AIS algorithm. Section 5 designs experiments and discusses the results. Section 6 concludes and presents future work.