Handover Research Articles

Support vector machine‐based handover scheme for heterogeneous ultra dense network of high‐speed railway

AbstractIn order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.

Equivalent Modeling and Analysis of Handover Process in K-Tier UAV Networks

Featured by flexible deployment, unmanned aerial vehicles (UAVs) can form multi-tier networks in 3D space, enhancing the capacity, but this heterogeneity causes more unnecessary handovers (HOs), i.e., ping-pongs (PPs) and handover failures (HOFs). However, existing studies have not modeled the HO process in multi-tier UAV networks. This paper proposes an equivalent model for the HO process in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -tier UAV networks, where nonlinear mapping rules are established from the altitude and transmit power to equivalent horizontal distances specific to HO events, then HO status during time-to-trigger (TTT) is analyzed via the mapping rules. Specifically, each trigger condition of the HO events is transformed into a circular boundary centered on the projection of the target UAV with the corresponding trigger radii. Considering the practical HO process, the HO trigger location is tractably modeled as a random variable of the path length from the start, and the signal-to-interference ratio during TTT and the residence time are analyzed through equivalent geometric relations. The results show that there exists an altitude ratio that causes the most frequent HO, which increases by 25% to 150% under different configurations. Trade-offs between HOF and PP exist in the altitude ratio and transmit power ratio, besides the HO parameters.

3D Boundary Modeling and Handover Analysis of Aerial Users in Heterogeneous Networks

Aerial user equipments (AUEs) levitating in the air are growingly used in various applications for the agility, but leads to link fluctuation and deterioration of handover (HO) performance, which is marked in heterogeneous networks (HetNets). However, existing HO models have not investigated HO performance of cellular-connected unmanned aerial vehicles (UAVs) under the influence of shadow fading at different altitudes. This paper proposes a 3D boundary model derived by trigger conditions to analyze HO performance of aerial users considering the shadow fading effect in two-tier HetNets, where the 3D HO trigger boundary and handover failure (HF) boundary are both in the form of a hemispherical shell with typical small base station (SBS) as center, composed of the point sets corresponding to the above HO events, which are derived by the Received Signal Strength (RSS) ratio of target SBS to source macro base station (MBS). Especially, the calculation of HO events probabilities under trigger conditions are transformed to the relationship between time-to-trigger (TTT) and during time inside 3D boundary by exploring the positional relationship between straight line and hemispherical shell boundaries. Thus, the compact expressions of HO probability, HF probability and PP probability are obtained to quantify the effect of 3D mobility and HO parameters in various deployment of HetNets. Theoretical results show that HF and PP probability dramatically drop by 68.5% and 47.1% when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H=20$</tex-math></inline-formula> m comparing to scenario of terrestrial users and HetNets deployment should be considered when choosing appropriate HO parameters.

An Equivalent Model for Handover Probability Analysis of IRS-Aided Networks

Thanks to the reflecting beamforming gain, Intelligent Reflecting Surface (IRS) is expected to increase coverage range and reduce unnecessary handovers (HOs). However, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O({{N^{2}}})$</tex-math></inline-formula> gains from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> elements and double path-loss change HO decision thus IRS cascaded channels need to be considered instead of the direct base station (BS)-user equipment (UE) channel, which makes existing Euclidean distance-based HO models not applicable. This paper proposes a uniform standard of equivalent UE-BS distance for deciding HOs with IRS cascaded channels, where the reference signal from the serving BS is reflecting beamformed while the reference signals from the neighboring BSs are randomly scattered due to the IRS being occupied. Specifically, the geometric point set of possible BS locations satisfying the HO condition is revised via the equivalent UE-BS distance. In addition, the IRS reselection affects the cascaded channel gain and HO decision, so the distribution of UE-IRS distance is modified with the correlation of UE location in unit time. The compact expression of HO probability in IRS-aided networks is obtained and the effect of the degree of IRS distributed deployment on HO is studied. The results show that there exists an optimal distributed deployment factor that minimizes the HO probability, which decreases by 59.6% compared to networks without IRSs when the density of BS is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{3}/\rm {km^{2}}$</tex-math></inline-formula> , the IRS serving distance is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20\rm {m}$</tex-math></inline-formula> and the number of IRS elements per cell is 100.

A Framework to Address Mobility Management Challenges in Emerging Networks

The key enablers for emerging cellular networks such as densification, concurrent operation at multiple bands and harnessing mmWave spectrum give birth to a peculiar set of new network management challenges. One such key challenge is the user mobility management. In this article, we identify the key issues that render current mobility management paradigm inadequate for delivering the expected Quality of Experience (QoE) and resource efficiency in emerging and future cellular networks. Together, these challenges call for paradigm shift in the way mobility is managed in cellular networks. We present an Advanced Mobility Management and Utilization Framework (A-MMUF) that can enable this paradigm shift by transforming mobility management from being a reactive to a proactive process. The core idea of A-MMUF is to build upon Mobility Prediction Models (MPMs) to predict various attributes of user mobility and traffic patterns such as next candidate cell for handover (HO), time of HO, future cell loads. These predictions are then leveraged to not only improve HO process for better QoE and less signaling overhead, but also to enable proactive automation to further maximize network performance in terms of energy and spectrum efficiency. However, understandably, the gains offered by the A-MMUF hinge on the accuracy of the MPMs, which may vary, not only with the choice of underlying machine learning technique, but also with the volume, variety, and fidelity of the training data. We analyze the potential gains of A-MMUF through three case studies: namely proactive HO, proactive Mobility Load Balancing (P-MLB), and proactive Energy Savings (P-ES). In addition to demonstrating significant gains in all three case studies, the results provide useful insights into the agility vs accuracy tradeoff that can be leveraged for choosing optimal machine learning models for practical deployment of A-MMUF.

Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G

In recent years, heterogeneous networks (HetNets) have drawn a lot of attention to connecting devices that will enable everything to become smart, efficient, and fast. These networks are made up of many cell types, including macro, micro, pico, and femto that are added to suit customer demand. HetNets requires sophisticated mobility management to handle a variety of inter-frequency technologies. Mobility management needs to be adequately addressed to prevent service degradation caused by high rates of unnecessary handover attempts (HOA), handover ping-pong (HOPP), handover failure (HOF), radio link failure (RLF) and HO delay involved, which necessitates the user to execute the handover (HO) process while moving from one place to another. A well-suited HO management technique is proposed to resolve the issues observed when the user moves. The purpose of this study is to ascertain how the handover control parameter (HCP) involves the functionality of the 5G network. The novel approach taken into consideration in this work for cell selection is proactive decision-making (PDM). The performance of the proposed technique is evaluated through a simulation consisting of 5G Hetnets. Comparisons of evaluations were made in terms of HOA, HOPP, HOF, RLF and HO delay.

An Overview of Mobility Management Mechanisms and the Related Challenges in 5G Networks and Beyond

Ensuring a seamless connection with various types of mobile user equipment (UE) items is one of the more significant challenges facing different generations of wireless systems. However, enabling the high-band spectrum – such as the millimeter wave (mmWave) band – is also one of the important factors of 5G networks, as it enables them to deal with increasing demand and ensures high coverage. Therefore, the deployment of new (small) cells with a short range and operating within the mmWave band is required in order to assist the macro cells which are responsible for operating long-range radio connections. The deployment of small cells results in a new network structure, known as heterogeneous networks (HetNets). As a result, the number of passthrough cells using the handover (HO) process will be dramatically increased. Mobility management (MM) in such a massive network will become crucial, especially when it comes to mobile users traveling at very high speeds. Current MM solutions will be ineffective, as they will not be able to provide the required reliability, flexibility, and scalability.Thus, smart algorithms and techniques are required in future networks. Also, machine learning (ML) techniques are perfectly capable of supporting the latest 5G technologies that are expected to deliver high data rates to upcoming use cases and services, such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). This paper aims to review the MM approaches used in 5G HetNets and describes the deployment of AI mechanisms and techniques in ″connected mode″ MM schemes. Furthermore, this paper addresses the related challenges and suggests potential solutions for 5G networks and beyond.

Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning

Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we investigate the privacy-preserving joint edge association and power allocation (JEAPA) problem in the face of the environmental uncertainty and the infeasibility of individual learning. Upon modelling the problem by a decentralized partially observable Markov Decision Process (Dec-POMDP), it is solved by federated multi-agent reinforcement learning (FMARL) through only sharing encrypted training data for federatively learning the policy sought. Our simulation results show that the proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.

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

Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input-multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research.

A comparative study of machine learning-based load balancing in high-speed train system

With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem.

Intelligent coordinated self-optimizing handover scheme for 4G/5G heterogeneous networks

Mobile connections and applications are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide, which greatly increases the demands on data traffic. This led to considering millimeter-wave bands and ultra-dense deployment as part of the key enabler solutions in fifth-generation (5G) networks. However, these solutions radically increase the number of handovers (HOs), thus increasing the rate of unnecessary HO and dropping call probabilities. In this regard, optimizing HO control parameters appropriately is the main factor that can efficiently address HO issues during user mobility. This paper proposes a fuzzy-coordinated self-optimizing HO scheme to achieve a seamless HO while users move in multi-radio access networks. The proposed scheme resolves the conflict between mobility robustness and load balancing functions by utilizing a fuzzy system considering three input parameters: signal-to-interference-plus-noise ratio, cell load and UE speed. Simulation results show that the proposed scheme manages to control the mobility optimization in terms of ping-pong HO, radio link failure and HO latency over different mobile speed scenarios. Moreover, the proposed scheme reduces the outage probability compared to other schemes from literature.

Seamless Handover in LEO Based Non-Terrestrial Networks: Service Continuity and Optimization

Developing non-terrestrial networks (NTN) in future wireless networks has been widely recognized to bring advanced communication services to remote and unserved areas. The Low-Earth-Orbit (LEO) constellation has emerged as a promising component for NTN to provide seamless and fast global connectivity. However, since natural dynamic features, the mobility management, in particular the handover (HO) between satellites, plays an important role in ensuring a stable and continuous data service for NTN. Motivated by this fact, this paper proposes a HO optimization strategy based on conditional handover (CHO) mechanism to enhance service continuity in LEO-based NTN. A reward function, related to link service time and service capability, is firstly designed to modify the monitoring conditions of target satellite candidates. The optimal target selection algorithm is proposed to obtain the maximum reward for each CHO. Then, a service continuity performance graph (SCG) model is constructed to predict different potential CHO combinations in service duration. On the basis of SCG, the HO sequence supporting a high-quality and stable data service is predictively calculated for each accessing user. Simulation results demonstrate that the proposed HO optimization scheme can obviously reduce handover rate under different NTN conditions and can better enhance NTN service continuity.

A Survey on Handover Optimization in Beyond 5G Mobile Networks: Challenges and Solutions

Handover (HO) management is essential in mobile cellular networks. It ensures seamless connectivity to the User Equipment (UE) while moving from one Base Station (BS) to another within the coverage area. HO optimization refers to the adoption of intelligent and automatic HO techniques in mobile networks. HO optimization is gaining more importance in Fifth-Generation (5G) and Beyond (B5G) systems because of the requirements and specifications that B5G targets. The requirements of B5G include global connectivity, ultra-low latency, big data analytics, extreme data rate transmissions, a massive number of devices in a small area, and new technologies that will support the B5G network, such as Millimeter Wave (mmWave), Terahertz (THz) communication, and Ultra-Dense Networks (UDNs). All these factors cause new HO optimization challenges and require new solutions for HO optimization techniques. This study comprehensively provides HO optimization challenges and solutions in B5G. First, it provides a research background and explanation of HO in legacy. It then investigates the HO optimization challenges in B5G, including future research directions. The paper then discusses the most prominent and recent techniques and technological solutions for HO optimization management in B5G. Finally, we highlight potential techniques for HO optimization in B5G.

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

Multi-Level Fuzzy Inference System Based Handover Decision Model for Unmanned Vehicles

In coming years, mobile communications systems will have a significant role for Unmanned Aerial Vehicles (UAVs), that will either function as mobile users or as mobile base stations in the sky. No doubt UAVs or drones provide a number of benefits for mobile communications networks and other non-communication services, but they also have a number of drawbacks, particularly when it comes to managing handover (HO). A number of approaches have already been suggested by various authors for making the HO easy and efficient, but only few parameters were considered for making the HO decision. However, with the advancement in technology and continuous rise in users, the handover system's reliance factors continue to grow, which could make the system more complex. As a result, the requirement for creating and implementing a system that can effectively manage system complexity arises. In order to reduce the complexity of the system, a novel method is proposed in this paper in which multi-level fuzzy system is used. The main goal of using the fuzzy system at different levels is to minimize the rule complexity of fuzzy systems at different levels which in turn enhances the performance of handover systems. In addition to this, number of parameters like coverage, speed limit, cost, connection time, security and power consumption were taken into consideration while designing the handover system. The model works in three levels, at the first level coverage, speed limit and cost parameters are processed to generate the first probability output. At the second level, connection time, security and power consumption parameters are considered to get the second probability output. The two outputs obtained thus serves as the input to third level where again they are processed to get the final output as estimation level. The efficacy of the suggested multi-level fuzzy system is analyzed in the MATLAB tool. The experimental results are obtained and compared with the traditional handover systems in terms of various dependency factors to prove its efficiency.

5G mmWave Multiple Active Protocol Stack Optimized Deep Learning Handover

To maintain seamless real-time connectivity for 5G applications, various handover (HO) schemes that can support a near-zero mobility interruption time (MIT) have been proposed. Due to the high frequency of mmWave signals, line-of-sight (LOS) is required for communication, because significant degradation and HO failure (HOF) occurs in non-LOS (NLOS) situations. In this letter, a novel deep learning based multiple active protocol stack (MAPS) optimized HO scheme is proposed to reduce the outage rate during the HO process and decrease the MIT. The performance evaluation results show that a much lower MIT and outage rate can be obtained when using the proposed MAPS HO scheme, compared to other HO techniques.

Blockchain‐enabled handover skipping for high mobility train passengers

Due to the cell size decrements and limited sojourn time on a high speed train mobility, unnecessary handovers (HOs) occur, which can lead to higher network communication costs, and affect passengers quality of service (QoS). This paper proposes a novel blockchain-enabled privacy preserving HO skipping framework by using train mobility dataset from the city of London. Using a complex dataset parameters, passenger traffic flows are modelled by averaging various train lines and station's footfall numbers utilising blockchain to maintain privacy. The framework stores pseudonym addresses in order to track the path of users. The proposed framework allows for a better trade-off in terms of 2% (approx.) gain in average throughput, over 100% gain in the last-hop signal quality, and a 50% reduction in HO costs, while also addressing the needs for resources to operate the blockchain.

A New Vertical Handover Prediction Method for Heterogeneous Wireless Networks

Long Term Evolution (LTE) is the fourth generation (4G) cellular network technology that provides improved performance that related to data rate, coverage and capacity compared to earlier cellular systems. The addition of many small cells in a heterogeneous network configuration provides a means to enhance capacity through extreme frequency reuse. The heterogeneous environment of different network technologies can provide high data rate and enhance multimedia services, but it is challenging to provide optimized handover (HO). In this paper, a new method is proposed to select the optimal network from available networks to which a UE may be handed over to achieve better QoS performance. The aggregation of multiple criteria for calculation of overall ranking of networks is obtained by Analytical Hierarchy Process (AHP) [1] and is combined with the history of previously visited cells and regression analysis of the Layer 1 (L1) and Layer 3 (L3) filtered Received Signal Strength (RSS) data for prediction of future values. The AHP is used to calculate the weights for the system attributes and to rank the available networks based on multiple criteria Multiple Attribute Decision Making (MADM). The sequence of visited cells is used in target network selection as it reduces frequent handover. The sequence of visited cells would be modeled as a Markov Chain. To assess RSS, beside L1, L3 filtering RSS prediction and smoothing is used to predict future signal samples to hasten the process of HO. RSS forecasting is used to predict handover necessity so as to reduce the handover delay. The results show that the number of handovers is reduced by up to 50% compared to the conventional AHP. The results also show that the threshold crossing rate and average duration of fades are reduced by 47% compared with the AHP. Handover delay is also reduced by up to 40 ms due to RSS forecasting.

Three-Phase Handover Management and Access Point Transition Scheme for Dynamic Load Balancing in Hybrid LiFi/WiFi Networks.

Since LiFi and WiFi do not interfere with one another, a LiFi/WiFi hybrid network may provide superior performance to existing wireless options. With a large number of users and constant changes, a network can easily become overloaded, leading to slowdowns and fluctuations in data transfer speeds. Handover (HO) increases significantly with an increase in users, which can negatively impact system performance and quality of service (QoS) due to connection loss and/or delay. Innovative three-phase handover management and AP transition (TPHM-APT) is proposed with the goals of maintaining a steady link with reduced HOs for all connected users, meeting high per-user data rates, and having low outage performance. The proposed scheme primarily focuses on reducing the total number of HOs, which improves reliability and keeps user densities low on individual LiFi APs, which conserves bandwidth and energy. Conventional methods of HO management and user assignment, such as those based on signal strength strategy (SSS), involve reallocating users to a different AP the moment they encounter a HO. Our technique consists of three stages that focus on the optical gain, the incidence angle of the receiver FOV, and user mobility speed for decision-making. Specifically, a data rate threshold (DRT), which is equivalent to the data rate gained from the optical gain, is used to determine whether users must be served by a LiFi or a WiFi AP. In addition, an incidence angle threshold (IAT) is identified to manage the handover process and user AP transition with the consideration of the user mobility threshold (UMT). The proposed method considers load balancing (LB) among all connected users as well. This approach is evaluated using Monte Carlo simulations with MATLAB. Mathematical expressions are derived to analyze the performance of the proposed method. Different aspects, for example, Outage Probability, HO Overhead, User density, System Average Throughput (SAT), and Average Data Rate Requirement (ADRR), are studied. Analysis shows performance gains in overall system performance in terms of system data rates, fairness, and HO rates. Simulation results show that against the standard HO scheme and traditional HO skipping and APA methods, the proposed scheme can effectively decrease HO rates, save LiFi resources, and increase user throughput. It also shows good correspondence to the analysis and reveals the associated trade-offs that occur when moving between the span of narrow to wide FOVs and vice versa (HO rates and APS). The proposed scheme achieves almost identical results for low-density and high-density systems as well, with different ADRR and HO overhead values.