# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
## 1 Introduction  
Heterogeneous networks (HetNets) have gained considerable attention in the past few years. These networks consist of different types of cells, such as macro, pico, and femto, which are introduced to meet user demand. As stated by the third generation partnership project (3 GPP), the main role of the HetNets is to improve the network performance in terms of boosting capacity and coverage [1]. In next generation cellular networks, an ultra-dense small cell, which comprises numerous small cells overlapped with macro cells, is introduced to boost coverage and improve user experience [2]. Non-standalone (NSA) and standalone (SA) are two road maps of transition from 4G to 5G mobile networks [3]. In the former, the 5G networks are deployed along with the existing 4G core network. Thus, the first stage of providing the 5G service starts with NSA. Then, 5G SA is implemented after the 5G coverage is completely established. Implementing a 5G network that overlaps with the 4G network leads to mobility issues when the users move from one base station (BS) to another [4, 5]. Mobility management in HetNets is complex due to several types of inter-frequency technologies involved, which requires the user to perform the handover (HO) process while moving from one BS to another. Although the mobility robustness optimization (MRO) function can improve the user mobility experience in 4G networks, further improvement is necessary to address and resolve the issues of the HO probability (HOP), HO ping-pong (HOPP), and HO failure (HOF). The improvementcan be implemented by optimizing the values of the HO control parameters (HCPs) according to user experience. Mobility management should be properly addressed to avoid service degradation due to high rates of HO, HOPP, and radio link failure (RLF). Furthermore, this issue should be resolved to ensure that the 5G of cellular networks can provide a seamless communication during user movement among different deployment scenarios [6; 7]. Several functions are introduced in self-optimization networks, such as MRO and load balancing optimization [8]. Both functions perform optimization to achieve different objectives during user's mobility and aim to dynamically optimize the HCP values to handle different HO problems. For example, the MRO function automatically adjusts the HCP values to maintain system quality and performs automatic optimization for HCPs with minimal human intervention. The HCP consists of two main parameters: HO margin (HOM) and time to trigger (TTT). By adjusting these parameters to the proper values during user movements in cell coverage, the rates of HOPP and HOF are minimized, consequently improving the service quality. Numerous studies have proposed different algorithms to address and solve the HO issue in HetNets. [PERSON] et al. [9] have optimized HCPs based on user equipment (UE) velocity and HO types using mobility state estimation (MSE). In this work, the authors only updated the TTT based on the user velocity with limited updating values of TTT. This method did not fully optimize the HO performance since the gap between these updating values is very big and only three fixed values are selected in accordance with UE velocity. Similarly, [PERSON] and [PERSON] [10] have presented an HO decision strategy and an MSE scheme to avoid unnecessary HOs (UHOs) and service failures in HetNets. The proposed model utilizes the number of HOs and sojourn time measurements to estimate the UE velocity. The simulation results show that the proposed MSE model reduces the number of UHOs and service failures. However, the performance with respect to other HO performance metrics, such as HOPP, RLF, and delay, have not been discussed. Researchers [11] have also introduced an adaptive algorithm that selects different values of HOM and load balancing for each UE in HetNets.