# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
###### Abstract
Techniques such as energy-efficient AI models, trajectory planning optimization for UAV-based networks, and distributed edge computing can significantly enhance energy efficiency in HO processes [11; 12]. Lastly, as 6G networks evolve, optimizing HO management remains a significant challenge that requires adaptive and innovative solutions. With respect to existing limitations and studies into future-proofed AI-based HO strategies, researchers can aid the establishment of effective and efficient HO mechanisms as well as cognitive HO methods. This can enable enhanced network efficiency and latency mitigation, as well as providing reliable connections to be offered across future smart cities. With respect to 6G's projected rapid adoption, greater emphasis in intelligent HO frameworks will be essential to develop the wireless communications networks of the future. In addition, the inclusion of security, energy efficiency, and adaptive decision-making in the management of HO will be necessary to address the multiple challenges offered by 6G environments. The design of robust, scalable, and intelligent HO solutions for enabling the seamless operation of next-generation wireless communication systems will necessitate an interdisciplinary and holistic methodology. This survey attempts to provide a comprehensive overview of HO management techniques in 6G networks and identify the critical challenges that should be addressed to enable seamless mobility in smart cities. Also, this paper aims to bridge these gaps by systematically reviewing intelligent handover techniques and identifying key limitations and future directions. Specifically, this research aims to achieve the following:  
* Examine current HO mechanisms in 6G networks, and their strengths, weaknesses, and effects on network performance. * Investigate AI- and ML-based HO approaches that utilize predictive modeling, real-time data analysis, and adaptive decision-making to optimize mobility management. * Investigate fuzzy logic-based HO methods and their contribution to enhancing decision accuracy in uncertain and dynamic network environments. * Investigate the challenges that are shaping HO management in 6G networks. * Identify future trends of HO management in 6G networks for smart cities. This work makes some contributions to the field of HO management in 6G networks. It first provides a systematic categorization of HO methods, dividing them into AI-based decision-making, fuzzy logic-based optimization, and network adaptability. Second, the research identifies some of the most important challenges related to HO management in ultra-dense 6G networks, such as high handover rates, security threats, and energy efficiency issues. By recognizing these critical challenges, the survey seeks to direct future research towards the development of more efficient and scalable HO solutions. Finally, this review is a valuable source of information for policymakers, network administrators, and researchers in that it provides an overview of the latest developments in HO strategies for 6G networks. By combining theoretical research with practical implementations, this research aims to provide insights into developing more intelligent, robust, and efficient mobility management solutions. The significance of this survey lies in its focus on adaptive and intelligent handover strategies tailored for ultra-dense, heterogeneous 6G networks in smart city environments. Unlike prior reviews that broadly cover mobility management or traditional handover algorithms, this work highlights emerging AI-fuzzy and hybrid mechanisms while also addressing overlooked aspects such as energy efficiency, security, and deployment scalability. The remainder of this paper provides an exhaustive overview of the topic discussed.