# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 1 Introduction  
The continuous increase in the use of mobile communications and cellular networks has created challenges related to the growth in load demand [1]. This situation has generated the need to introduce specialized mechanisms to address these issues and prevent service quality degradation [2]. In this complex environment, artificial intelligence (AI) plays a significant role, particularly within the technology and telecommunication sectors [3]. Load balancing emerges as a fundamental tool for the implementation of mobile networks, especially in areas with a high user density. It involves optimizing aspects such as signal quality, traffic management, resource allocation and other parameters that could impact network performance [4]. Load balancing is a comprehensive field that involves a variety of meta-heuristic techniques and methods, including machine learning models, resource allocation algorithms and deep learning, among others [5]. Moreover, load balancing optimization is necessary to manage the unpredictable load conditions caused by uneven data usage and cell imbalances [6]. This approach seeks to improve operational efficiency, quality of service (QoS) and user experience by enabling networks to adapt dynamically and proactively to changing resource demand behaviors [7]. The introduction of 5G brings about highly complex network management scenarios, leading to the increased utilization of Self-Organizing Networks (SONs). As a result, many users who expect to maintain a certain quality of experience (QoE) are poorly served, and their user throughput is subjected to decision-making processes for adjusting classic parameters of the mobile network based on SON systems. The problem that arises when making network adjustments is the lack of decisions based on user behavior, their distribution, location, and multiple parameters that can be obtained from context awareness [8]. With zero latency requirement of 5G networks, SONs need to change from reactive to proactive response through the holistic context in addition to the traditional context [9]. Mobility robustness optimization (MRO) and mobility load balancing (MLB) are two distinct approaches to load balancing with different objectives. MLB is designed for macrocell scenarios and aims to address persistent congestion issues caused by factors like population growth in urban areas. On the other hand, MRO focuses on minimizing undesirable effects, such as dropped calls, resulting from the handover (HO) process that enables users to move freely within the network. Both concepts aim to enhance network efficiency and ensure equitable distribution of radio resources. They employ algorithms to balance the load of users among cells, effectively mitigating adverse effects [10]. Classic parameters such as handover margins (HOMs), time to trigger (TTT), antenna tilt angles, and power traffic sharing have been used for years in the processes of self-healing and load balancing in mobile networks and fuzzy logic controllers [11]. These systems have been widely adopted due to their low data processing overhead, and they can be combined with socially aware information to identify user crowds across event locations, thereby reducing data overload by leveraging knowledge of each user's location in the radio scenario [12]. Machine learning-based algorithms have also played a significant role in recent years in automatic implementations for changes in classic and context-aware parameters. A typical implementation involves the use of fuzzy logic combined with the Q-learning algorithm to take actions that are less reliant on expert knowledge and more adapted to thereal circumstances of the network, considering various possible outputs for the same input state [13]. This study defines three classes of algorithms by level of autonomy in next-generation mobile networks.