# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 1 Introduction
Automated algorithms (e.g., deep reinforcement learning) operate without human intervention, targeting latency-sensitive scenarios requiring millisecond decisions (e.g., URLLC for industrial automation, autonomous vehicles). Semi-automated approaches combine predefined rules with ML-based adjustments, excelling in dynamic mMTC environments (e.g., stadiums, IoT densification) where operators adjust parameters based on real-time KPIs. Hybrid systems integrate predictive AI with emergency heuristic solutions, making it possible to address volatile 5G/6G use cases, such as network segment reconfiguration or edge computing handoffs, where reliability-critical transitions require coordination between humans and AI. Supervised learning techniques and deep reinforcement learning have also been implemented in predicting cellular data usage [14], taking into account mobility models adapted to human behavior or real traces obtained from the mobile network [15], as well as information from social sources such as calendars, Twitter and open databases, among others. Despite the considerable number of studies about load balancing in mobile networks [16], very few have conducted comparisons of automatic or semi-automatic control methods incorporating social awareness. However, implementing a load balancing system is quite challenging due to the various possibilities of controllers, radio environment characteristics, traffic schemes, user mobility models and their adaptability to different social behaviors. Furthermore, an important limitation lies in maintaining the confidentiality of user location data and calculating location with respect to network-specific positioning techniques adds further overhead. In this context, our objective is to conduct a systematic review of the literature focused on load balancing techniques. The main purpose of this review is to identify and examine the most-used techniques, as well as evaluating their feasibility across different study scenarios. Our intention is not limited to determining the most common practices in the field of load balancing in mobile networks but also verifies their applicability and performance in diverse case studies. In addition, extensive research has been presented in several bibliographic sources focused on work related to optimization systems for load balancing in mobile networks. In this study, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique [17], which involves the collection and synthesis of evidence from multiple studies to address specific research questions. The remainder of this paper is divided into several sections. Section 2 sets out the research objectives. Section 3 addresses the methodology, in which the research questions are posed and the literature collected from various sources is presented, along with the inclusion and exclusion criteria following the PRISMA method. Section 4 includes graphs summarizing certain properties of the studies, as well as a summary and analysis of each paper selected from the database. Section 5 provides a discussion aiming to identify commonalities among the publications in relation to specific topics.