A Systematic Literature Mapping of Machine Learning for Handover Optimization in Cellular Networks

Handover (HO) is a fundamental process in cellular networks (CNs) that ensures continuous connectivity as users move across different coverage areas. With the evolution of mobile communication technologies, the application of Machine Learning (ML) to optimize HO has emerged as a promising research avenue. However, the current literature is fragmented, spanning various cellular standards, ML approaches, datasets, and evaluation practices. This paper presents a comprehensive Systematic Literature Mapping (SLM) to consolidate and structure existing research on ML-based HO optimization. A total of 2,772 articles were identified through a carefully validated search strategy, from which 86 primary studies were selected based on strict inclusion and exclusion criteria. The analysis reveals a predominant focus on LTE (53.8%) and 5G (44.1%), a high reliance on simulated datasets (82.6%), and the frequent use of reinforcement learning (46.5%) for tasks such as handover prediction (45%), ping-pong reduction (22%), and base station selection (10%). Despite these advancements, critical gaps persist, including under-use of real-world data, limited exploration of legacy networks such as 3G, and insufficient methodological standardization. This study contributes a structured synthesis of the field, highlights research trends and deficiencies, and offers actionable insights to guide future work in ML-driven mobility management for evolving CNs.