Handover Management Research Articles

Handover management in vehicle communication: applications, techniques, issues, and challenges: a review

Vehicle-to-everything (V2X) communication is an emerging technology that facilitates communication among vehicles and numerous environmental entities. However, it encounters certain challenges throughout the handover process. This study analyses the challenges and complexity of managing handover, specifically in maintaining uninterrupted connection and meeting the service criteria outlined in the 3GPP 5G new radio (NR) standard. Various applications of V2X technology that require handover management are explored, such as vehicle safety and traffic management, enhanced driver assistance, and autonomous driving. Furthermore, this paper illuminates the most recent developments in V2X communication, highlighting the significance of efficient handover management, resolving technical issues, based on the full potential of the use of V2X apps that contribute to the establishment of a transportation ecosystem that is characterized by enhanced safety, increased intelligence, and improved connectivity. This paper can be used as a starting point for thinking about how to improve C-V2X communication.

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Handover management procedures for future generations mobile heterogeneous networks

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

A Review on Deploying Blockchain Technology for Network Mobility Management

In wireless mobile networks, Mobility Management (MM) is a critical aspect that involves different tasks such as handover management, location management, offloading, resource allocation, and others. Blockchain platform comprising of an array of linked blocks innately supports the incorruptibility, sustaining the authenticity, and sustain pseudonymous privacy. Traditional MM is centralized, in contrast to blockchain-grounded MM that is partially or fully decentralized in its approach. As the inaugural researchers to review on blockchain-grounded MM, we are proud to cluster the blockchain-grounded MM concept under 7 clusters and closely study each framework with respect to different aspects such as blockchain specific parameters, MM specific parameters, network parameters, and more. We gathered a sample of 92 source references by curating the articles for eligibility criteria examined from electronic archives utilizing a comprehensive and enduring way. Grounded on this inspection, blockchain-grounded MM reinforces the overall security, trust, and privacy of location management and resource allocation, blockchain-grounded secure handover, facilitating registration and authentication, automatic MM utilizing smart contracts, reducing vulnerabilities, repairing low quality data, coordination, fair scheduling, in offloading, and blockchain-grounded resource allocation. Close studying proves that out of blockchain grounded MM frameworks, 37.5% utilize blockchain grounded secure handover, 92.5% utilize simple blockchain architecture, 32.5% utilize PoW consensus, 100% distributed MM, 97.5% host grounded MM, 95% packet reordering tolerant MM, 52.5% QoS aware MM, and 12.5% proposed for 5G networks. Finally, we present the openings and obstructions of the concept of blockchain-grounded MM and then catering surmounting advices.

MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

A Channel Perceiving-Based Handover Management in Space–Ground Integrated Information Network

A Channel Perceiving-Based Handover Management in Space–Ground Integrated Information Network

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

AbstractMillimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.

A New Method of Intelligent Handover management in 5G Communication Networks-IHMCN

A New Method of Intelligent Handover management in 5G Communication Networks-IHMCN

Multi-Agent Deep Reinforcement Learning for Handover Management in Massive Industrial Internet of Things Networks

The industrial internet of things (IioT) is considered one of theIapplications in the fifth generation (5G) networks. In this application, users’ high mobility in a typical industrial scenario needs high reliability. The high mobility creates frequent handover, creating extra control signalling to a new base station (BS). The users’ congestion to the new BSs can lead to an outage. In this paper, we investigate how to manage the handover of users to improve reliability in a high-mobility scenario using deep learning. We first use an offline centralized algorithm to create labels for user association to a BS that is done without adding handover coefficient. Then, we train the neural network and use the trained parameter to make the multi-agent deep reinforcement learning (RL) learns better. This is done to avoid long iterative methods in reinforcement learning. The results show that our method can outperform the offline centralized algorithm by 40% when the handover coefficient increases.

User Preference-Based Heterogeneous Network Management System for Vertical Handover

Vertical handover management plays an essential role in wireless network technologies, mainly due to the rapid development of various radio access technologies (RATs) that require users to connect seamlessly from one RAT to another. However, in multiple RAT environments, vertical handover management encounters different challenges, including unnecessary handovers, handover failures, ping-pong handovers, and unsuitable access network selection. Essential in vertical handover management is maintaining the desired quality of service (QoS) by the mobile device user. The seamless movement of mobile device users as they run various applications depends on a well-performing vertical handover decision-making algorithm. This bears special significance in a heterogeneous network environment. This paper proposes a vertical handover algorithm that considers user preferences (i.e., a vertical handover algorithm that evaluates the application currently running on a user device). The main objective of the algorithm is to determine when it is necessary to perform the handover, depending on the applications running on the mobile device. The proposed algorithm utilizes a fuzzy logic system to assess whether the handover is necessary and a multiattribute decision-making (MADM) method to select the best available radio access network. A simulation scenario involving different applications at various mobile device velocities was developed. The results proved the algorithm’s effectiveness compared to some of the earlier proposed vertical handover algorithms. At velocities below 10 m/s and 30 m/s, the proposed algorithm had 0% and 15.02% unnecessary handovers, respectively, while the technique for order preference by similarity to ideal solution (TOPSIS) utility’s function-based algorithm obtained 12.38% and 23.24% at the same velocities, respectively. In addition, compared to TOPSIS, the obtained results of the proposed algorithm demonstrated a lower handover failure rate and ping-pong rate for a velocity span of 1–30 m/s for the considered user applications.

Rate-stabilized and load-balanced handover management for subway hybrid LiFi and WiFi networks

In this paper, a subway interchange mobility (SIM) model is first proposed to simulate the interchange behavior of users in the subway scenario. Then, an event-triggered distributed network architecture is presented and employed in the subway hybrid light-fidelity (LiFi) and wireless-fidelity (WiFi) networks (HLWNets). Next, a rate-stabilized and load-balanced handover management scheme for subway HLWNets is developed. By using the event triggering mechanism, the proposed architecture can significantly reduce the measurement reports uploaded by users. By the proposed handover method, not only the network load can be balanced with very low complexity, but also the data rate of users is stabilized without compromising network throughput. Finally, by simulation, the effectiveness of the proposed method has been demonstrated. Simulation results show that the proposed architecture reduces up to 92.67% in the number of measurement reports compared to the conventional periodical structure. Moreover, the proposed handover method decreases the fluctuation index by 13.5% and 25%, respectively, compared to the standard and separate optimization algorithm (SOA)-based handover methods. Besides, the proposed method also has significant advantages in terms of Jain's fairness index and user satisfaction.

MILIMETER-WAVE MASSIVE MIMO: A PROMISING TECHNOLOGY FOR NEW AND EMERGING NETWORKS

This paper presents a review of the mm Wave technology and the massive MIMO technology first as individual technologies detailing their potentials and challenges. It also presents a review of the combined technologies, their channel models, various spectrum, interference and handover management schemes as a promising technology for new and emerging networks.

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.

Correction to: Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-Dense Cellular Network

Correction to: Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-Dense Cellular Network

Multiplicative long short-term memory-based software-defined networking for handover management in 5G network

Multiplicative long short-term memory-based software-defined networking for handover management in 5G network

Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network

Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network

A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions

With the rapid growth of data traffic and mobile devices, it is imperative to provide reliable and stable services during mobility. Heterogeneous Networks (HetNets) and dense networks have been identified as potential solutions to address the upcoming capacity crunch, but they also pose significant challenges related to handover optimization. This paper presents a comprehensive review of recent handover decision algorithms in HetNets, categorizing them based on their decision techniques and summarizing their input parameters, techniques, and performance evaluations. Our study highlights the technical challenges and opportunities related to handovers in HetNets and dense cellular networks and provides key findings from recent studies. The significance of this survey is to provide a comprehensive overview of handover decision algorithms in HetNets and dense cellular networks, which can aid in the development of more advanced handover optimization approaches.

Secure and Efficient Multicast-Enabled Handover Scheme Pertaining to Vehicular Ad Hoc Networks in PMIPv6

In VANET, mobility management and handover management are two of the most intriguing and challenging research topics. The existing mobility management infrastructures are unable to provide seamless secure mobility and handover management. It is very common in a vehicular network that when a vehicle roams between two domains, its reachability status may be compromised. The main reason for this is the higher handover latency and packet loss during the handover process. In the last decade, IP-based mobility protocols have been proposed for interoperable handover management systems. There has been a great deal of interest in providing IP multicast to mobile nodes such as vehicles, and numerous strategies have been put forth thus far. This research article proposes an IP multicast-enabled handover architecture for VANET in PMIPv6. Adding the IP multicast facility to the authentication server allows handover management that is both intra-domain and inter-domain, which originally was not supported by PMIPv6. This makes it possible for the IP service of a vehicle to maintain a connection from any location, without changing the earlier application. Additionally, a secure architecture with authentication capabilities built on top of PMIPv6 is suggested for VANET to address the authentication problem. Finally, the article compares the performance of the proposed architecture with that of the ones currently in use by varying several factors, including the vehicle’s density, the setup costs required, and the unit transmission costs on wired and wireless links, and it shows that our proposed solution ensures the handover process with a minimal cost change.

Fuzzy Logic Based Handover Authentication in 5g Telecommunication Heterogeneous Networks

Under various deployment circumstances, fifth-generation (5G) telecommunications delivers improved network compound management with fast communication channels. Due to the introduction of the Internet of Things (IoT) in data management, the majority of the ultra-dense network models in 5G networks frequently have decreased spectral efficiency, weak handover management, and vulnerabilities. The majority of traditional handover authentication models are seriously threatened, making them vulnerable to a variety of security attacks. The authentication of networked devices is the most important issue. Therefore, a model that incorporates the handover mechanism and authentication model must be created. This article uses a fuzzy logic model to create a handover and key management system that focuses on cloud handover management and authentication performance. In order to decrease delays in 5G networks, the fuzzy logic is built with multiple criteria that aim to reduce the number of executed handovers and target cell selection. The simulation is run to evaluate the model’s performance in terms of latency, spatial complexity, and other metrics related to authentication attack validation.

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.