Search Results (21)

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

Fifth-generation Vehicle-to-Everything (V2X) networks have ushered in a new set of challenges that negatively affect seamless connectivity, specifically owing to high user equipment (UE) mobility and high density. As UE accelerates, there are frequent transitions from one cell to another, and handovers (HOs) are triggered by network performance metrics, including latency, higher energy consumption, and greater packet loss. Traditional HO mechanisms fail to handle such network conditions, requiring the development of Intelligent HO Decisions for V2X (IHD-V2X). By leveraging Q-Learning, the intelligent mechanism seamlessly adapts to real-time network congestion and varying UE speeds, thereby resulting in efficient handover decisions. Based on the results, IHD-V2X significantly outperforms the other mechanisms in high-density and high-mobility networks. This results in a reduction of 73% in unnecessary handover operations, and an 18% reduction in effective energy consumption. On the other hand, it improved handover success rates by 80% from the necessary handover and lowered packet loss for high mobility UE by 73%. The latency was kept at a minimum of 22% for application-specific requirements. The proposed intelligent approach is particularly effective for high-mobility situations and ultra-dense networks, where excessive handovers can degrade user experience. Full article

The deployment of Augmented Reality (AR) is a necessity as an enabling technology for intelligent transportation systems (ITSs), with the potential to boost the implementation of Vehicle-to-Everything (V2X) networks while improving driver experience and increasing driving safety to fulfill AR functionality requirements. In this regard, V2X networks must maintain a high quality of service AR functionality, which is more challenging because of the nature of 5G V2X networks. Moreover, the execution of diverse traffic requirements with varying degrees of service quality is essential for seamless connectivity, which is accomplished by introducing efficient handover (HO) techniques. However, existing methods are still limited to basic services, including conversional, video streaming, and general traffic services. In this study, a Multiple Criteria Decision-Making (MCDM) technique is envisioned to address the handover issues posed by high-speed vehicles connected to ultra-high-density (UDN) heterogeneous networks. Compared with existing methods, the proposed HO mechanism handles high mobility in dense 5G V2X environments by performing a holistic evaluation of network conditions and addressing connection context requirements while using cutting-edge applications such as AR. The simulation results show a reduction in handover delays, failures, and ping-pong, with 84% prevention of unnecessary handovers. Full article

Handover is crucial for ensuring seamless connectivity in heterogeneous networks (HetNet) by enabling user equipment (UE) to switch its connection link between cells based on signal conditions. However, conventional analytical approaches ignored the distinctions between macro-cell to small-cell (M2S) and small-cell to macro-cell (S2M) scenarios during a handover decision-making process, which resulted in handover failures (HoF) or ping-pong handovers. Therefore, this paper proposes a novel framework, Do-SPN-PPP, that combines stochastic Petri net (SPN) and the Poisson point process (PPP) to quantitatively analyze M2S and S2M handover performance differences. The proposed framework also reveals and predicts how handover parameters affect UE residence time in a cell within the HetNet, and it exhibits a higher predictive accuracy compared with the traditional conventional analytical approach. In addition, the Monte Carlo simulation verified the Do-SPN-PPP framework, and the proposed framework exhibits a 96% reduction in computation time while maintaining a 95% confidence interval and 0.5% error tolerance compared with the simulation. Full article

In 5G networks and beyond, managing handovers (HOs) becomes complex because of frequent user transitions through small coverage areas. The abundance of small cells (SCs) also complicates HO decisions, potentially leading to inefficient resource utilization. To optimize this process, we propose an intelligent algorithm based on a method that utilizes a fuzzy logic controller (FLC), leveraging prior expertise to dynamically adjust the time-to-trigger (TTT), and handover margin (HOM) in a 5G ultra-dense SC heterogeneous network (HetNet). FLC refines TTT based on the user’s velocity to improve the response to movement. Simultaneously, it adapts HOM by considering inputs such as the reference signal received power (RSRP), user equipment (UE) speed, and cell load. The proposed approach enhances HO decisions, thereby improving the overall system performance. Evaluation using metrics such as handover rate (HOR), handover failure (HOF), radio link failure (RLF), and handover ping-pong (HOPP) demonstrate the superiority of the proposed algorithm over existing approaches. Full article

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed. Full article

This paper presents an overview of the findings in the handover (HO) process performance within three routes in Quito, Ecuador. We used the Net-Monitor Software to gather information from one of the three national mobile operators. Then, we used the R tool to analyze the HO performance. We analyze several performance metrics, such as HO types, HO conditions, and the ping-pong process. Analysis of the results of the outdoor drive tests demonstrate that the radio frequency (RF) parameters, such as Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), power margin, times radio frequency measurements repeats, and HO percentage to nearest BS, are extremely important during different HO types and ping-pong processes because there are statistical differences in these measured RF parameters. The main measurement results demonstrate that RSSI difference between inter HO and intra HO is 20 dB, whereas HOs are performed when the mobile device (MS) gets farther from the base station (BS), approximately 50% of total HOs. Operator achieves a high ping-pong rate of approximately 10% of total HOs. Full article

The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs. Full article

A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth’s orbit at a constant speed. Ground services from continuously moving satellites cause frequent handovers. In addition, frequent handovers may come as a load between User Equipment (UE) and the communication system, which leads to degradation of service quality. Unlike Terrestrial Networks (TN), communication services are provided to UEs at altitudes ranging from 20 km to 35,584 km, rather than from base stations close to the ground. Service at high altitudes is unreliable due to the measurement values that were previously used as quality indicators to operate terrestrial networks. Moreover, service at high altitudes demands long-distance communication, and propagation delay occurs from the long-distance communication. In the 3GPP Rel. 17 document, it is suggested that the above problems should be solved. This paper tries to solve the problem by proposing the two-step XGBOOST, a CART-based Gradient Boosting Model. Handover in TN uses measurement-based conditional handover (CHO), but the measured values in the NTN environment are not valid. Using this, the distance between the UE and the center of the cell and the elevation angle are used to construct a model that predicts the HO triggering time point. In order to overcome the propagation delay caused by communication at a high altitude, a model that predicts the distance and elevation angle between the UE and the center of the cell considering the propagation delay is proposed. The model is composed of two-step XGBOOST. The one-step model is a model in which the UE predicts the distance and elevation angle between cell centers after propagation delay at the time when satellite position information is transmitted to the UE. The two-step model predicts handover triggering occurrence based on the data predicted by the one-step result. As a result of the experiment, the model considering the propagation delay showed about 8% better performance on average than the model not considering the propagation delay, and the XGBOOST model achieved an average F1-score of 0.9891 in the propagation delay experiments. Full article

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. Full article

Next generation mobile networks are expected to integrate multiple drones organized in Flying Ad Hoc Networks (FANETs) to support demanding and diverse services. The highly mobile drones should always be connected to the network in order to satisfy the strict requirements of upcoming applications. As the number of drones increases, they burden the network with the management of signaling and continuous monitoring of the drones during data transmission. Therefore, designing transmission mechanisms for fifth-generation (5G) drone-aided networks and using clustering algorithms for their grouping is of paramount importance. In this paper, a clustering and selection algorithm of the cluster head is proposed together with an efficient Group Handover (GHO) scheme that details how the respective Point of Access (PoA) groups will be clustered. Subsequently, for each cluster, the PoA elects a Cluster Head (CH), which is responsible for manipulating the mobility of the cluster by orchestrating the handover initiation (HO initiation), the network selection, and the handover execution (HO execution) processes. Moreover, the members of the cluster are informed about the impending HO from the CH. As a result, they establish new uplink and downlink communication channels to exchange data packets. In order to evaluate the proposed HO scheme, extensive simulations are carried out for a next-generation drone network architecture that supports Internet of Things (IoT) and multimedia services. This architecture relies on IEEE 802.11p Wireless Access for Vehicular Environment (WAVE) Road Side Units (RSUs) as well as Long-Term Evolution Advanced (LTE-A) and IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX). Furthermore, the proposed scheme is also evaluated in a real-world scenario using a testbed deployed in a controlled laboratory environment. Both simulation and real-world experimental results verify that the proposed scheme outperforms existing HO algorithms. Full article

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. Full article

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. Full article

Multi-access edge computing (MEC) is a key technology in the fifth generation (5G) of mobile networks. MEC optimizes communication and computation resources by hosting the application process close to the user equipment (UE) in network edges. The key characteristics of MEC are its ultra-low latency response and real-time applications in emerging 5G networks. However, one of the main challenges in MEC-enabled 5G networks is that MEC servers are distributed within the ultra-dense network. Hence, it is an issue to manage user mobility within ultra-dense MEC coverage, which causes frequent handover. In this study, our purposed algorithms include the handover cost while having optimum offloading decisions. The contribution of this research is to choose optimum parameters in optimization function while considering handover, delay, and energy costs. In this study, it assumed that the upcoming future tasks are unknown and online task offloading (TO) decisions are considered. Generally, two scenarios are considered. In the first one, called the online UE-BS algorithm, the users have both user-side and base station-side (BS) information. Because the BS information is available, it is possible to calculate the optimum BS for offloading and there would be no handover. However, in the second one, called the BS-learning algorithm, the users only have user-side information. This means the users need to learn time and energy costs throughout the observation and select optimum BS based on it. In the results section, we compare our proposed algorithm with recently published literature. Additionally, to evaluate the performance it is compared with the optimum offline solution and two baseline scenarios. The simulation results indicate that the proposed methods outperform the overall system performance. Full article

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. Full article