Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

Abstract

Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors' knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

Optimal Handover Optimization in Future Mobile Heterogeneous Network Using Integrated Weighted and Fuzzy Logic Models

“…MRO has attracted considerable interest from the research community. For instance, surveys [7], [8], and [20] have discussed the HO self-optimization, mainly the MRO functions. We have addressed several summary tables related to MRO studies in our surveys [7] and [8].…”

“…For instance, surveys [7], [8], and [20] have discussed the HO self-optimization, mainly the MRO functions. We have addressed several summary tables related to MRO studies in our surveys [7] and [8]. Each study in these tables includes a solution method, scenario, mobility model, HCPs, KPIs, simulator, and achievements.…”

“…To avoid the redundancy work done in [7] and [8], several up-to-date MRO studies have been addressed in this study such as in [4], [22], and [61][62][63][64][65][66][67]. Besides, Table 1 summarizes various studies by addressing the problem, system, HCPs, mobility model, KPIs, solution method, drawback, and the operating frequency for each study.…”

“…However, various algorithms with different deployment scenarios, simulators, and key performance indicators (KPIs) have been developed all through the past years. Therefore, several conventional and artificial intelligence optimization methods have been applied, as addressed in these works [7], [8].…”

A Survey on Handover and Mobility Management in 5G HetNets: Current State, Challenges, and Future Directions

“…Moreover, the authors discuss the challenges and limitations of SDN-based HO optimization approaches and suggest potential areas for future research. The article [41] provides a state-of-the-art survey of MRO studies that utilize ML techniques, addressing the methodology used, HO criteria, HCPs, KPIs, simulators, and accomplishments. The study also discusses MRO challenges for intra-system and inter-system mobility and highlights issues for future investigations.…”