# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 1 Introduction  
The widely deployed fifth generation (5G) and the coming sixth (6G) communication networks are believed to offer a large amount of network services for growing diverse demands, such as high-definition video playing, vehicular networks, massive Internet of Things, and telemedicine [1; 2; 3]. These demands are generated by traditional mobile communication applications and vertical market segments with different requirements for quality of service (QoS) and key performance indicators (KPI) [4; 5; 6; 7]. It is challenging to achieve novel communication performance by a \"one-size-fits-all\" network infrastructure for various requirements of QoS and KPI. In the past several years, network slicing, supported by a software-defined network (SDN) and network function virtualization (NFV), is a promising technique in future communications [8; 9; 10]. By extracting communication network resources into different levels, efficient network slicing can provide customized network services according to different network QoS requirements [11; 12]. Incorporated with machine learning algorithms, network slicing can significantly increase the performance of cellular networks in different scenarios and applications [13; 14; 15; 16]. However, nearly all emerging communication techniques should be implemented on the telecommunication equipment manufactured by different vendors [17; 18]. In general, vendors provide large amounts of parameter configurations to fit for different scenarios. Taking the perspective of the telecommunication operator, these parameter configurations should be carefully designed by jointly considering the specific scenario and equipment capacity [19; 20; 21]. Unified network parameter configuration may not always achieve novelperformance when traffic loads are burst and varying in different scenarios, such as high population density areas, emergencies, and high-speed mobility [22; 23]. For example, imbalanced distribution of traffic loads among cells and extremely high traffic loads in certain areas of cells tend to occur in large-scale concert. On the other hand, different scenarios exhibit different characteristics of traffic loads, which require parameter configuration tuning to satisfy the high requirement of user experience and to achieve novel network resource allocation. The majority of academic research works in communication networks focus on efficient algorithms to enable performance optimization both in physical and medium access control (MAC) layers. In [24], the authors summarized the main techniques of resource allocation in 5G heterogeneous networks, including macro-, micro-, pico-, and femto-cellular networks. More specific scenarios beyond 5G and 6G are discussed in [25; 26]. The authors in [25] discussed energy consumption issues within a massive Internet of Things (IoT) system. Various dynamic network architectures and corresponding algorithms are presented in detail. For 6G spectrum utilization, the authors in [26] presented a big data processing algorithm to improve spectrum utilization for 6G cellular and IoT networks. However, there are few works related to the refinement of parameter configurations of cellular networks based on field data in practice, which becomes a practical and challenging issue that telecommunication operators have to solve. In this paper, taking the perspective of a telecommunication operator, we propose a framework of network feature analysis and configuration refinement for cellular networks. Moreover, we analyze traffic features of different scenarios with high traffic loads by considering the field data. The significant indicators of high traffic load cells are identified and associated with performance metrics. According to these indicators, we propose different configuration refinement polices that are discussed in detail in several typical scenarios, such as high population density, emergencies, and high-speed mobility. The corresponding performance evaluation is also provided for different scenarios with the proposed refinement polices.