## 2 Related Surveys  
Number of surveys on mobility management in 5G wireless communication had already been published and cited in Table 1. In [11], the survey reviews key mobility elements i.e., HO, cell selection, cell reselection and challenges of cellular communication due to transmitted power discrepancy and coverage area of cells in heterogeneous network (HetNet) deployment. Authors also introduces algorithms and strategies to mitigate these challenges. In [12], authors review the applications of DRL in modern cellular networks such as Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks. Authors describe how network entities (UE, Drone) learn optimal policies for i.e., HO and offloading decision, cell and channel selection and reselection, reliable connectivity of multi-UAV network, and mobility pattern with the help of DRL. In [13], authors discussed research issues such as mobility management, resource allocation, data offloading and ultra-reliable low-latency communication (uRLLC) in multi-agent reinforcement learning (RL) framework for vehicle-to-everything (V2X) scenarios. Authors also discussed the prospective applications of MARL framework for decentralization and scalability in optimal decision policy. In [14], authors review the fundamentals of machine learning in 5G networks applications, i.e., small cells and heterogeneous networks, massive MIMO, massive MTC, energy harvesting, smart grid, and so on. Authors also emphasize the usage of machine learning algorithms for the future networks to explore the advance applications and services. In [15], authors present comprehensive review on 5G enabling technologies with UD networks. Authors also discuss the research challenges in intelligent management techniques and back haulsolutions for 5G wireless networks such as unnecessary HO, unfairness in radio resource sharing, energy consumption, severe interference, and degraded quality-of-service (QoS). In [16], authors discuss in depth the mobility management in long term evolution (LTE) and 5G new radio (NR) with HetNets cellular networks. Authors also discuss HO performance metrics, HO failure types, differences from LTE to NR, mobility enhancers, and potential research challenges and techniques related to HO management. In [17], authors discuss UAV-based communications and characteristics with the help of ML techniques and present all relevant research works. Authors also consider physical layer issues such as channel modeling, interference management, transmission parameters configuration, physical layer security, resource management, and position related aspects. In [18], authors discuss evolution of 5G wireless networks with architectural modifications combined with radio access network (RAN). Authors also discuss the mmWave technology comprising massive MIMO technologies, channel model estimation, beamforming algorithms, and directional antenna design. Authors focused on SON features, energy awareness and cost efficiency, QoS, QoE combined with the 5G evolution. At the end, authors discuss research issues with future directions, simulation experiments, relevant field trials, and drive tests. In [19], authors reviews HO algorithms for 60 GHz networks and with other bands. The comparison of HO algorithms is supposed for commendation of algorithm for each network. First, authors discuss 60 GHz based wireless systems then requirements, resource management, different schemes, and issues in 60 GHz based Wireless Systems. The research paper [20] focused on applications of DRL in resource management for 5G heterogeneous networks. Authors discussed the 5G architecture, heterogeneous networks, resource management functions for 5G HetNets. Then they discussed the DRL based resource management for 5G HetNets, comparative summary and analysis and open issues and future directions. In [21], authors discussed 3 GPP based HO procedure with related KPIs, and challenges in UDSC mobile networks.