# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 1 Introduction  
Coinciding with the development of 5 th generation (5G) technology, users have higher data requirements and are widely distributed [1]. Due to the uneven distribution of data requirements, the frequency resource utilization in any system will be also unevenly distributed. Cells with high resource utilization are called overloaded cells. These cells have less available resources, which may affect the quality of service (QoS) for users. Load balancing means to transfer some traffic from overloaded cells to other available cells to balance the frequency resources among cells. It is an important method to optimize resource utilization and improve QoS. Reference [2] reviews the historical developments of load balancing and provides guidance and a roadmap for developing load balancing. However, there is a growing demand for high-rate communication in remote areas with sparsely deployed terrestrial cells. In these areas, the frequency resources are insufficient, such that the cells are more likely to overload. What is more, neighboring cells found by traditional load balancing schemes may not suit being the offloading target cells due to the limitation of coverage. Since neighboring cells are not always available, reference [3] suggests using cells with high coverage to achieve load balancing. According to the architecture of the future network [4], non-terrestrial cells will provide coverage for these suburban, rural and island areas. Therefore, this paper studies an effective scheme for a satellite-terrestrial integrated network (STIN) to solve the overloading problem. According to the different radio access technologies (RAT) in networks, load balancing is divided into intra-RAT load balancing and inter-RAT load balancing. Some work hasfocused on intra-RAT load balancing: In [5], a load assignment policy and a target selection policy are proposed, which utilize the matching theory. In [6], traffic was transferred from overloaded cells to the neighboring cells with less load to guarantee seamless handover in 5G systems. In [7], for giant low-Earth-orbit satellite networks, the authors designed two different handover methods for users with predictable handover times and users with unpredictable handover times to ensure load balance. In [8], the authors used fuzzy logic to adjust the cell individual offset (CIO) parameter in the handover process, so that overloaded cells were more likely to trigger handover and the neighboring cells were more likely to accept handover users. In [9], the authors also focused on CIO, but they introduced reinforcement learning to suit dynamically changing environments. These schemes focus on the optimization of handover events. Without considering the characteristics of satellite coverage, these schemes may not be suitable for the STIN [10]. For inter-RAT load balancing, a heterogeneous network consisting of small cells and traditional macro cells is one of the typical research scenarios. The authors of [11] proposed a two-step mechanism based on two biases for RAT selection. The authors of [12] proposed an algorithm that adapts handover margins and time to trigger. Reference [13] proposes two different versions of simulated annealing to improve load balancing and spectral performance. On the other hand, STIN, which is expected to achieve seamless coverage and transmission, has also introduced many new challenges for current systems [14]. The authors of [15] used a content popularity and Stakelberg game model to propose an effective scheme for load balancing between unmanned aerial vehicle cells and macro cells. Reference [16] proposes an efficient scheme for load balancing by using Knapsack and Zipf. Reference [17] formulates the problem as a constrained, multi-objective linear programming problem to maximize the utilization efficiency between satellites.