Handover Research Articles

A novel approach for optimization of handover mechanism using metaheuristics algorithms

The new generation wireless system is an integrated global network, which provides many services for end users across the globe. The major requirement of end users is to allow the mobile user to roam seamlessly across the network. Heterogeneous Wireless Networks (HWNs) permit users with global devices to access the required service at any place and anywhere. The HWNs contain many Radio Access technology RATs such as GPRS (General Packet Radio Services), UMTS (Universal Mobile Telecommunication Service), Wi-Fi (Wireless Fidelity), Wi-Max (Worldwide Interoperability for Microwave Access), WLAN (Wireless Local Area Network) and transferring the ongoing call and providing seamless mobility can be achieved by Vertical Handover (VHO) mechanisms. The heterogeneous wireless networks (HWN) integrated with the network characteristics, like the bandwidth, speed support, cost, security, power consumption, delay, and range of communication are employed for obtaining better Quality of Services (QoS). For better mobility management, handover (HO) is the most significant aspect considered for solving the issues regarding mobility the designed Novel approach uses metaheuristics algorithms like grey wolf optimization (GWO) and Mayfly optimization (MFO) technique to provide a better service for end-user in terms of energy consumption, and handover delay, Call drop probability and throughput.

Reinforcement-Learning Based Handover Optimization for Cellular UAVs Connectivity

The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learningbased algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Novel group handover mechanism for Cooperative and Coordinated Mobile Femtocells technology in railway environment

Recently, the Mobile Femto (MF) Technology has been debated in many research papers to be a promising solution that will dominate future networks. This small cell technology plays a major role in supporting and maintaining network connectivity, enhancing the communication service as well as user experience for passengers in High-Speed Trains (HSTs) environments. Within the railway environment, there are many MF Technologies placed on HSTs to enhance the train passengers’ internet experience. Those users are more affected by the high penetration loss, path loss, dropped signals, and the unnecessary number of Handovers (HOs). Therefore, it is more appropriate to serve those mobile users by the in-train femtocell technology than being connected to the outside Access Points (APs) or Base Stations (BSs). Hence, having a series of MFs (called Cooperative and Coordinated MFs -CCMF) installed inside the train carriages has been seen to be a promising solution for train environments and future networks. The CCMF Technologies establish Backhaul (BH) links with the serving mother BS (DeNB). However, one of the main drawbacks in such an environment is the frequent and unnecessary number of HO procedures for the MFs and train passengers. Thus, this paper proposes an efficient Group HO mechanism that will improve signal connection and mitigate the impact of a signal outage when train carriages move from one serving cell to another. Unlike most work that uses Fixed Femtocell (FF) architecture, this work uses MF architecture. The achieved results via Matlab simulator show that the proposed HO scheme has achieved less outage probability of 0.055 when the distance between the MF and mobile users is less than 10 m compared to the signal outage probability of the conventional HO scheme. More results have shown that the dropping calls probability has been reduced when mobile users are connected to the MF compared to the direct transmission from the eNB. That is in turn has have improved the call duration of mobile UEs and reduced the dropping calls probability for mobile users who are connected to the MF compared to eNB direct connection UEs.

Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks.

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics.

Mobility management of multi-hop mobile integrated access and backhaul network

As services and applications utilizing mobile networks grow exponentially, base station (BS) density becomes increasingly dense. There is an increasing demand for wireless backhaul technology for efficient deployment of high-density mobile networks. Although integrated access and backhaul (IAB) technology for efficient operation of wireless backhaul is attracting attention, consideration for mobile IAB (MIAB) is very insufficient. In this work, we propose a novel handover (HO) scheme for MIAB network to reduce handover interruption time (HIT) and radio link failure (RLF) that have a significant impact on users' quality of service (QoS). We investigate HO cases that cover intra-gNB HO, inter-gNB HO, and parent MIAB node HO, and develop their probabilistic models according to the velocities of parent MIAB node and child MIAB node. In addition, we investigate the latency in uplink (UP) control plane (CP) data transmission and each HO case for the baseline MIAB network. Our proposed HO scheme consists of low-latency UL CP data transmission with semi-persistent resource pre-allocation and RACH-less HO procedure for child MIAB nodes. Through simulation, we verify our proposed MIAB HO scheme outperforms the baseline HO scheme in terms of HO delay and HO overhead.

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

Intelligent Dual Active Protocol Stack Handover Based on Double DQN Deep Reinforcement Learning for 5G mmWave Networks

The recently proposed dual active protocol stack (DAPS) handover (HO) is one of the mobility enhancements that can effectively reduce the handover interruption time (HIT) in 5G networks. By using a DAPS solution, users can be connected to both the source cell and target cell during the HO execution phase, and thereby 0 ms of HIT becomes possible. However, the DAPS HO procedure may fail in 5G networks due to the channel characteristics of millimeter-wave (mmWave) signals. Since mmWave links are vulnerable to blockages, the received signal quality may degrade suddenly, which gives rise to an abrupt outage before DAPS HO can be completed. In this paper, a novel learning-based DAPS HO technology named intelligent DAPS (I-DAPS) HO is proposed to avoid sudden radio link failure (RLF) while providing a high data rate. The proposed I-DAPS HO uses a double deep Q-network (DDQN) deep reinforcement learning (DRL) framework, where blockage predictions are made based on past received signal data such that RLFs can be actively avoided. The performance evaluation demonstrates that the proposed I-DAPS HO scheme can effectively avoid RLF and improve the throughput performance compared to advanced 5G HO schemes.

Intelligent Handover Algorithm for Vehicle-to-Network Communications With Double-Deep Q-Learning

For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (U.K.) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

Effectiveness of Handover Control Parameters on Handover Performance in 5G and beyond Mobile Networks

Mobility management is essential in mobile communication networks to provide a smooth connection during users’ mobility. Handover control parameters (HCPs), such as handover margin (HOM) and time-to-trigger (TTT), are major and essential factors in mobility management that must be defined carefully to make efficient handover (HO) procedures. Their impact becomes more critical with the deployment of fifth-generation (5G) mobile networks and beyond (B5G). This is due to the different characterizations of next mobile networks, such as the use of millimeter-wave (mmWave) bands, the ultradense deployment of small base stations (BSs), large mobile connection traffic growth, and other more critical factors. The case becomes more sensitive with the high mobility speed scenarios. This study proposes different HCP system settings to be investigated and analyzed over B5G networks. They will be investigated with various mobile speed scenarios to illustrate their impact on the network performance. Various key performance indicators (KPIs) are considered to evaluate and validate system performance, such as reference signal received power (RSRP), HO probability (HOP), HO ping-pong (HOPP), radio link failure (RLF), HO interruption time (HIT), and HO failure (HOF). Results show that the various system settings provide different and significant impacts on the performance of B5G networks. Furthermore, the setting of HCP1 obtained the best performance in RSRP and RLF with -69.7 dBm and 4.8%, respectively, while the optimum performance of HOPP, HIT, HOP, and HOF is achieved in the HCP6 setting with 0%, 0.02 ms, 0.05%, and 0%, respectively. Moreover, the overall outcome of all HCP settings is 54.94%. These results indicate a tradeoff between RLF and HOPP with various HCP settings in B5G mobile networks. Thus, the HCP system settings must be adjusted carefully considering other factors, such as mobile environment and use case.

Handover Management in 5G Vehicular Networks

Fifth-Generation (5G) vehicular networks support novel services with increased Quality of Service (QoS) requirements. Vehicular users need to be continuously connected to networks that fulfil the constraints of their services. Thus, the implementation of optimal Handover (HO) mechanisms for 5G vehicular architectures is deemed necessary. This work describes a scheme for performing HOs in 5G vehicular networks using the functionalities of the Media-Independent Handover (MIH) and Fast Proxy Mobile IPv6 (FPMIP) standards. The scheme supports both predictive and reactive HO scenarios. A velocity and alternative network monitoring process prepares each vehicle for both HO cases. In the case of predictive HO, each time the satisfaction grade of the vehicular user drops below a predefined threshold, the HO is initiated. On the other hand, in the case of reactive HO, the vehicle loses the connectivity with its serving network and connects to the available network that has obtained the higher ranking from the network selection process. Furthermore, the HO implementation is based on an improved version of the FPMIPv6 protocol. For the evaluation of the described methodology, a 5G vehicular network architecture was simulated. In this architecture, multiple network access technologies coexist, while the experimental results showed that the proposed scheme outperformed existing HO methods.

An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks

The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.

Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

Fifth-generation (5G) cellular networks offer high transmission rates in dense urban environments. However, a massive deployment of small cells will be required to provide wide-area coverage, which leads to an increase in the number of handovers (HOs). Mobility management is an important issue that requires considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with current fourth-generation (4G) networks. Although mobility robustness optimization (MRO) and load balancing optimization (LBO) functions have been introduced in the 3GPP standard to address HO problems, non-robust and nonoptimal algorithms for selecting appropriate HO control parameters (HCPs) still exist, and an optimal solution is subjected to compromise between LBO and MRO functions. Thus, HO decision algorithms become inefficient. This paper proposes a conflict resolution technique to address the contradiction between MRO and LBO functions. The proposed technique exploits received signal reference power (RSRP), cell load and user speed to adapt HO margin (HM) and time to trigger (TTT). Estimated HM and TTT depend on a weighting function and HO type which is represented by user status during mobility. The proposed technique is validated with other existing algorithms from the literature. Simulation results demonstrate that the proposed technique outperforms existing algorithms overall performance metrics. The proposed technique reduces the overall average HO ping-pong probability, HO failure rate and interruption time by more than 90%, 46% and 58%, respectively, compared with the other schemes overall speed scenarios and simulation time.

On the Impact of Rotational Mobility in Directional Cellular Systems

The high data rates required for next generation applications necessitate the use of millimeter wave and terahertz bands where bandwidth is abundant. Due to high path loss in these bands, antenna arrays (AAs) are needed to focus the signal in highly directional beams in the desired directions. However, highly directional communication links in these bands are vulnerable to misalignment and blockages due to mobility. Thus, both mobile blockers and user equipment (UE) rotations can significantly increase the handover (HO) frequency, while HO delays and HO failures jeopardize system latency and reliability. Furthermore, the scanning angle of each AA is limited by the orientation of the device, the mounting angle of the AA, the element spacing and the grating sidelobes formed during beamforming, which is referred as Field-of-View (FoV). In scenarios with UE rotational mobility, limited FoV may lead to loss of connection with the source next-generation NodeB (gNB). In this paper, we analyze current HO and radio link monitoring protocols in scenarios with UE rotational mobility and mobile blockers. We use a Markov Chain based analytical model as well as MATLAB based system level simulations to show how user rotation can significantly increase the outage duration under various deployment configurations. We propose enhancements to enable faster HO under user rotations and demonstrate significant performance improvements using simulations.

Handover Solutions for 5G Low-Earth Orbit Satellite Networks

Low-Earth orbit (LEO) satellite networks are meant to be fundamental to closing the digital divide, enabling new market opportunities and providing fifth-generation (5G) New Radio (NR) connectivity everywhere at any time. Despite the advantages of LEO deployments, these systems are characterized by a high mobility and a challenging propagation channel that compromise several procedures of the current 5G standards. One of the impacted areas is the radio mobility management, which is used to ensure continuous and satisfactory service while users handover among cells. Current research shows that the measurement-based 5G NR handover (HO) procedures, designed for terrestrial networks, fail to ensure optimal mobility performance. In this work, we provide a mobility performance analysis through extensive system-level simulations of state-of-the-art HO procedures for 5G NR over LEO satellite networks with Earth-moving cells. Furthermore, this article presents a novel antenna gain-based HO solution for intra-satellite mobility that exploits the predictability of the satellites movement and the antenna gain of the satellite beams, making user equipment (UE)’s radio measurements obsolete. The system-level simulation results, which consider users in rural and urban scenarios, show that by exploiting the known satellite’s trajectory, the UE eliminates service failures and undesired HO events, maximises the time-of-stay in a cell and experiences improved downlink signal-to-interference-plus-noise ratio. This article also includes a sensitivity study of the impact on the mobility performance of satellite-specific and UE-specific errors such as the UE’s location error, the satellite beam’s antenna radiation error and the satellite’s pointing error. Finally, the impact of the UE’s mobility is analyzed.

Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey

Ensuring reliable and stable communication during the movements of mobile users is one of the key issues in mobile networks. In the recent years, several studies have been conducted to address the issues related to Handover (HO) self-optimization in Heterogeneous Networks (HetNets) for Fourth Generation (4G) and Fifth Generation (5G) mobile networks. Various solutions have been developed to determine or estimating the optimum and ideal settings of Handover Control Parameters (HCPs), such as Time-To-Trigger (TTT) and Handover Margin (HOM). However, the complexity, high requirements, and the upcoming structure of ultra-dense HetNets require more advanced HO self-optimization techniques for future implementation. This paper studies HO self-optimization techniques that may implemented in the next-generation mobile HetNets by reviewing state-of-the-art algorithms. The solutions discussed in this survey are more focus on Mobility Robustness Optimization (MRO), which is a significant self-optimization function in 4G and 5G mobile networks. The applied solutions will preserve the continuous connection between the User Equipment (UE) and eNBs during UE mobility, thereby enhancing connection quality. The various algorithms and techniques applied to HO have revealed different outcomes. This paper discusses the pros and cons of these techniques, and further examines HO self-optimization challenges and solutions. New future directions for the implementation of HO self-optimization are also identified. This survey will contribute to the understanding of the issues related to mobility management, particularly in relation to the self-optimization of HO control parameters in future mobile HetNets.

Fast Data Recovery for Improved Mobility Support in Multiradio Dual Connectivity

Data aggregation is one of the crucial features of the 3GPP Multi-Radio Dual Connectivity (MR-DC) technology. However, mobility events and radio link failures, which may occur during the data aggregation, may pose challenges in meeting the latency, reliability, and throughput key performance indicators (KPIs). Unlike single connectivity, the user equipment (UE) in MR-DC operation can experience such events in either of the two base stations (BSs) serving the UE with MR-DC. In typical MR-DC deployments, these events occur more frequently in the BS acting as the secondary node (SN) since the SN operates at a higher frequency band. In this paper, we show that handovers (HOs) and signal blockage events that occur at the SN can create out-of-order data reception or losses at the UE’s Packet Data Control Protocol (PDCP) layer, making the application stop receiving data for up to hundreds of milliseconds. Thus, challenging to meet the KPIs defined for such application. To mitigate this effect, we propose an intelligent and efficient mechanism that operates in the transmitting PDCP layer and significantly minimizes the data interruption periods suffered by the application when the UE aggregates data and HOs or failures of the SN occur. We use LTE/NR testbed experiments to show that the proposed mechanism achieves a high and stable aggregate throughput with near-zero interruption time and data reliability of at least five nines without transport layer retransmissions. The experiments are conducted for saturated TCP traffic and under link quality variations based on traces extracted from a Nokia-proprietary system-level simulator.

Post-Disaster Communications: Enabling Technologies, Architectures, and Open Challenges

The number of disasters has increased over the past decade where these calamities significantly affect the functionality of communication networks. In the context of 6G, airborne and spaceborne networks offer hope in disaster recovery to serve the underserved and to be resilient in calamities. Therefore, our paper reviews the state-of-the-art literature on post-disaster wireless communication networks and provides insights for the future establishment of such networks. In particular, we first give an overview of the works investigating the general procedures and strategies for facing any large-scale disaster. Then, we present technological solutions for post-disaster communications, such as the recovery of the terrestrial infrastructure, installing aerial networks, and using spaceborne networks. Afterwards, we shed light on the technological aspects of post-disaster networks, primarily the physical and networking issues. We present the literature on channel modeling, coverage and capacity, radio resource management, localization, and energy efficiency in the physical layer part, and discuss the integrated space-air-ground architectures, routing, delay-tolerant/software-defined networks, and edge computing in the networking layer part. This paper also includes interesting simulation results which can provide practical guidelines about the deployment of ad hoc network architectures in emergency scenarios. Finally, we present several promising research directions, namely backhauling, cache-enabled and intelligent reflective surface-enabled networks, placement optimization of aerial base stations (ABSs), and the mobility-related aspects that come into play when deploying aerial networks, such as planning their trajectories and the consequent handovers (HOs).

ZEUS: Handover algorithm for 5G to achieve zero handover failure

In 5G, the required target for interruption time during a handover (HO) is 0 ms. However, when a handover failure (HOF) occurs, the interruption time increases significantly to more than hundreds of milliseconds. Therefore, to fulfill the requirement in as many scenarios as possible, we need to minimize HOF rate as close to zero as possible. 3GPP has recently introduced conditional HO (CHO) to improve mobility robustness. In this study, we propose “ZEro handover failure with Unforced and automatic time-to-execute Scaling” (ZEUS) algorithm to optimize HO parameters easily in the CHO. Analysis and simulation results demonstrate that ZEUS can achieve a zero HOF rate without increasing the ping-pong rate. These two metrics are typically used to assess an HO algorithm because there is a tradeoff between them. With the introduction of the CHO, which solves the tradeoff, only these two metrics are insufficient anymore. Therefore, to evaluate the optimality of an HO algorithm, we define a new integrated HO performance metric, mobility-aware average effective spectral efficiency (MASE). The simulation results show that ZEUS provides higher MASE than LTE and other CHO variants.

Real-time high-speed mobility management

High-speed mobility system has now become a serious concern for mobile operators due to the large frameworks of a heterogeneous network made up of multiple cell types and different frequency bands. Handover (HO) is conducted in a real-life scenario when the user equipment (UE) moves from one network coverage to another by performing proper measurement with high speed. HO breakdown and call loss are observed due to a high speed; thus, high-speed mobility system needs improvement by using the UE speed as one of the key measurement monitoring criteria for the long-term evolution (LTE) network. Vendor consultation has been considered in this paper in addition to real drive test measurement in highways. Results have shown that velocity has a direct impact on the handover quality and overall timing. Results also demonstrate that 120 km/h measurement is better than 140 km/h as UE speed.

An enhanced LTE handover scheme for high‐speed railway application

AbstractHigh‐speed railway (HSR) is one of the most important modes of passenger transport with many advantages, such as; safety, high capacity, short intervals, and comfort. To provide high speed mobile data to the passengers of HSR for infotainment services, the best option is LTE or LTE‐R. The LTE‐R is a railway specialized network under development. The very high speed of trains in HSR, which are far faster than conventional commuters, counteracts the seamless data service and deteriorates the QoS which is mainly due to handover (HO) failure when the user equipment (UE) is moving very fast. Conventional LTE HO schemes are not designed for very high speed of HSR. Some research studies were done to provide HO schemes with more adaptation with the HSR condition but the desired specifications are not fully satisfied. In this article, we have devised a new S3 scheme for LTE HO based on time‐to‐trigger parameter in which the required ETSI's HO success rate standard at very high speed moving UE is met with a good safety margin. In addition, the presented scheme shows better performance in terms of HO latency, reference signal received power gap and handover operation probability. To do the job, an accurate mathematical analysis was developed for the proposed S3 scheme, conventional A3 and A4 schemes and the H2 scheme that we found to be the best one out of previous research studies. The results obtained have been verified by simulations.