A systematic review of handover optimization techniques in 5G self-organizing networks

The emergence of 5G technology, along with the rapid expansion of ultra-dense small cell (UDSC) deployments, has significantly increased the complexity of mobility management. Handover (HO) management is a fundamental function of wireless networks that ensures uninterrupted service continuity when a user equipment (UE) moves across different coverage areas or base stations. 5G UDSC environments require efficient handover management for maintaining stable connectivity and high-quality user experience. The effectiveness of handover management depends on the proper adjustment of Handover Control Parameters (HCPs), named as Time-to-Trigger (TTT) and Handover Margin (HOM), these two parameters collectively determine when and how a handover should occur. This study presents a systematic literature review (SLR) that examines handover optimization strategies within 5G Self-Organizing Networks (SON). This study follows the guidelines provided by Kitchenham and the PRISMA framework, with coverage of publications from 2015 to 2025. In this research HO is categorized into three main categories namely, traditional, AI/ML-based, and hybrid approaches. Traditional methods include rule-based, fuzzy logic based, velocity-based, and weighted based techniques that give predictable and efficient solutions, but these approaches are limited by static thresholds and reduced adaptability in dynamic network scenarios. AI/ML-based methods such as supervised learning, reinforcement learning, and deep learning introduce adaptive and predictive tuning of HCPs parameters that result in reductions of handover failures and ping-pong effects and also improve throughput, however, these methods require extensive datasets and computational resources. Hybrid approaches combine the stability of traditional techniques with the adaptability of AI/ML that results in outperforming of single method solutions but still face challenges such as higher complexity and lack of standardization. Most reviewed studies rely on simulation environments such as MATLAB and ns-3, with only a few validated through real-world experimentation or focused specifically on UDSC settings. Overall, this research leads towards the future direction on adaptive, context-aware, and scalable frameworks, emphasizing the need for online learning, hybrid AI integration, privacy-preserving optimization, and large-scale real-world testing to advance intelligent handover management in next-generation networks.