Mobility Management In Networks Research Articles

A Novel Link-Network Assignment to Improve the Performance of Mobility Management Protocols in Future Mobile Networks

5G is expected to support new services and applications that will change the user experience and will drive to a new business landscape. Moreover, most of the services will require optimum connectivity and seamless mobility in heterogeneous networks. To cope with these challenges, network mobility management and network densification are envisioned to be key factors in the emerging 5G architectures. By deploying a large number of cells, 5G architectures can provide users with high throughput and flexible access services, an improvement of the network scalability and optimized network coverage. However, with this densification, seamless mobility support can lead to significant increasing in signaling overhead due to frequent handovers. In this context, network operators need to efficiently plan the deployment of the base stations taking into account the mobility management of the users, and the service degradation that this mobility process could cause. This article aims to optimize the assignment of the base stations to the access routers in the mobile network to improve the network performance. The results obtained show that our proposed Link-Network Assignment algorithm based on clustering techniques achieve significant gains in terms of signaling and data forwarding costs. These simulation results demonstrate that the proposed algorithm can successfully reduce signaling cost and packet delivery cost by up to 56% and 5%, respectively, on average, compared with baseline algorithms.

Post-Quantum Era in V2X Security: Convergence of Orchestration and Parallel Computation

Along with the potential emergence of quantum computing, safety and security of new and complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous, and secure operation of these scenarios, quantum-resistant security algorithms (QRSAs) that enable secure connectivity must be integrated into the network management and orchestration systems of mobile networks. This article explores a road-map study of post-quantum era convergence with cellular connectivity using the Service & Computation Orchestrator (SCO) framework for enhanced data security in radio access and backhaul transmission with a particular focus on vehicle-to-everything services. Using NTRU as a QSRA, we show that the parallelization performance of the Toom-Cook and Karatsuba computation methods can vary based on different CPU load conditions through extensive simulations, and that the SCO framework can facilitate the selection of the most efficient computation for a given QRSA. Finally, we discuss the evaluation results, identify the current standardization efforts, and present possible directions for the coexistence of post-quantum and mobile network connectivity through an SCO framework that leverages parallel computing.

Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks

Many registration schemes have been proposed to reduce the signaling cost required for user’s mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze a registration scheme that could deal with mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on mobility prediction and corresponding TAL forms, a new TAL was constructed such that the registration cost could be minimized. In addition, a semi-Markov process model was newly presented for the registration scheme considering mobility prediction and corresponding flexible TAL forming for two different environments: urban and rural. Simulation studies were also performed to validate the accuracy of the semi-Markov process model. Numerical results showed that analytical and simulation results were very close (average relative error of 1.4%). The registration cost decreased as the moving probability (q) to the predicted direction increased. The performance of the proposed scheme was superior to distance-based registration (DBR) or TAL-based scheme especially when q was high. When call-to-mobility ratio was less than or equal to 1 corresponding to current small cell configurations, the proposed scheme outperformed the DBR or TAL-based scheme.

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.

Neural Networks for Energy-Efficient Self Optimization of eNodeB Antenna Tilt in 5G Mobile Network Environments

In this paper, we present an energy-efficient Self Organizing Network (SON) architecture based on a tunable eNodeB (eNB) antenna tilt design for macrocells in a mobile network environment. This is an imperative element of mobility management in high speed and low latency wireless networks. The SON architecture follows a fully distributed approach with optional network information exchange with neighboring cells and core network. Antenna tilt directly affects its radiation pattern thus changes in eNB antenna tilt can be used to optimize cell coverage and reduce interference in mobile networks. We apply and compare two reinforcement machine learning techniques for optimizing the eNB antenna tilts, i.e., Deep Q-learning using Artificial Neural Network (ANN) and a simple Stochastic Cellular Learning Automata (SCLA). ANN is well known for its ability to learn from a vast number of inputs, while the stochastic learning technique relies on a simple action based probability vector updated based on system feedback. Neighboring cells for any one cell in the network environment are selected based on their separation distance and antenna orientation. We validate the data call performance of the network for edge users as they directly impact the Quality of Service (QoS) in the mobile environment. Our simulated results show that ANN performs better for edge users as compared to SCLA. The model also satisfies the SON requirement of scalability and agility. This work is a follow-up to our earlier work, where we showed that SCLA performs better than Q-learning in a similar network environment and optimizing strategy due to its low complexity, but within the same Q-learning algorithm more input learning parameters gave better performance.

Metaheuristics on quantum computers: Inspiration, simulation and real execution

Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.

Quality of service management for an intent-based heterogeneous network using mobile QoE application

Traditional Service Level Agreement (SLA) based on the quality of service (QoS) management methods are insufficient to ensure quality-related contracts between service providers and users. This article proposes a user-centric method for QoS management in heterogeneous mobile networks. Based on a new QoE metric on a scale of 1 to 5, this method considers the commercial value of electronic services to end-users. With this approach, the configuration and functionality of the network automatically change depending on the requirements of the end-users. The work proposes a conceptual model for constructing an intent-based software-defined heterogeneous network, which effectively manages shared resources and adapts to users’ needs. A prototype of a mobile and operator application for adaptive client-oriented service delivery in a heterogeneous network has been developed, which makes it possible to obtain the ordered QoE based on the feedback between the user and the network operator. Using this approach will allow network operators to provide individualization of service users with a certain level of QoS by analyzing their estimates of QoE (ordered through the developed mobile application). And the use of machine learning algorithms will allow to react to unfavorable combinations of values of quality indicators and prevent the situation when the user is not satisfied with the quality of services received for adaptive prediction of the moment of network reconfiguration. We propose a method for managing the QoS provision in a heterogeneous wireless network using Big Data technology and a mobile QoE application, which considers and analyzes the estimates of the ordered QoE and allows users QoS improvement according to the demand. It is demonstrated that using the proposed method in a heterogeneous wireless network allows reducing the number of dissatisfied users with the quality of service by up to 60% using an experimental study.

Method for Artificial KPI Generation With Realistic Time-Dependent Behaviour

Machine Learning (ML) is the dominating solution for the implementation of Self-Organizing Networks (SON), which automate mobile network management. However, the data scarcity derived from the reluctance of operators complicates the necessary training phase ML algorithms. In this letter a method to generate artificial Key Performance Indicators (KPIs) time series is proposed considering their time-dependent behaviour. The data is modelled and categorised according to the time of the day and the data models are adapted with statistical copulas to create samples which present interrelation among different KPIs. Finally, results obtained from a real mobile network are presented.

Modeling of Wireless Traffic Load in Next Generation Wireless Networks

Software defined WiFi network (SD-WiFi) is a new paradigm that addresses issues such as mobility management, load management, route policies, link discovery, and access selection in traditional WiFi networks. Due to the rapid growth of wireless devices, uneven load distribution among the network resources still remains a challenging issue in SD-WiFi. In this paper, we design a novel four-tier software defined WiFi edge architecture (FT-SDWE) to manage load imbalance through an improved handover mechanism, enhanced authentication technique, and upgraded migration approach. In the first tier, the handover mechanism is improved by using a simple AND operator and by shifting the association control to WAPs. Unauthorized user load is mitigated in the second tier, with the help of base stations (BSs) which act as edge nodes (ENs), using elliptic ElGamal digital signature algorithm (EEDSA). In the third tier, the load is balanced in the data plane among the OpenFlow enabled switches by using the whale optimization algorithm (WOA). Moreover, the load in the fourth tier is balanced among the multiple controllers. The global controller (GC) predicts the load states of local controllers (LCs) from the Markov chain model (MCM) and allocates packets to LCs for processing through a binary search tree (BST). The performance evaluation of FT-SDWE is demonstrated using extensive OMNeT++ simulations. The proposed framework shows effectiveness in terms of bandwidth, jitter, response time, throughput, and migration time in comparison to SD-WiFi, EASM, GAME-SM, and load information strategy schemes.

EDiPSo: An Efficient Scalable Topology Discovery Protocol for Software-Defined Vehicular Networks

With the vast data loads produced by numerous applications and services, network softwarization became a vital addition to the design of intelligent heterogeneous vehicular networks. Smart Software-Defined Vehicular Networks (S-SDVN) are promising solutions that support various applications and protocols such as network mobility management, routing, traffic control and provides a centralized innovative decision-making protocol. SDVNs remove the limitations imposed by traditional hardware-based network devices to offer a global view of the networks’ status and give a standard framework for heterogeneous wireless technologies (WiMAX, WiFi/6, 5G, 6G). However, to capture the network’s topology and manage data traffic, an efficient discovery protocol is needed to mitigate the challenges under vehicles’ rapid mobility. In this article, we present an efficient and scalable topology discovery protocol for distributed software-defined vehicular networks, which we refer to as EDiPSo. The proposed protocol reduces the overhead and time complexity of the discovery process based on the centrality score measurements and vehicle traffic densities over switch-enabled roadside units. EDiPSo protocol is evaluated and compared to existing benchmark solutions using realistic mobility traces of Shenzhen City. The results showed that the proposed distributed discovery process significantly reduces the overhead compared to other protocols and decreases the time required to complete a discovery cycle as the number of locally distributed controllers increases.

Mobility management in multi-tier LiFi networks

Mobility management is an important part of the analysis and design of ultra-dense LiFi networks. This paper thus presents a two-tier LiFi network and analyses the cross-tier handover rate between the primary and secondary cells. For different conditions of semiangle at half illuminance of the primary and secondary cells, we propose three different coverage models for the secondary cells. Using stochastic geometry, closed-form expressions are derived for the cross-tier handover rate, ping-pong rate, and sojourn time in terms of the received optical signal intensity, time-to-trigger, and user mobility. The analytical models are validated with simulation results.

A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks

Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

A Service-Centric Q-Learning Algorithm for Mobility Robustness Optimization in LTE

Due to the diversity of mobile services and rising user expectations, mobile network management has changed its focus from Quality of Service (QoS) to Quality of Experience (QoE). As a consequence, classical network optimization procedures must be updated accordingly. One of these optimization procedures is Mobility Robustness Optimization (MRO), whose aim is to improve HandOver (HO) performance by reducing HO failures. In this work, a novel QoE-aware MRO algorithm is proposed considering a multi-service scenario. Unlike previous approaches, whose aim is to increase successful handover rates, the optimization aim in this work is two-folded: to improve cell edge QoE while improving successful handover rates in the whole network. For this purpose, the handover trigger point, defined by the pair of HO control parameters HO margin and Time to Trigger, are tuned on a per-adjacency basis according to QoE and HO failure measurements. Method assessment is based on a dynamic system-level simulator implementing a realistic LTE scenario with multiple services. Results show that the proposed QoE-aware MRO algorithm improves cell edge QoE throughout the network while increasing the percentage of successful handovers compared to traditional approaches.

Efficient mobility management in IP networks through three layered MIPv6

Mobile IPv6 (MIPv6) is considered inefficient to support mobility due to larger handover delay and signaling overhead. Therefore, Hierarchical MIPv6 (HMIPv6) is designed by introducing a Mobile Anchor Point (MAP) in the MIPv6 architecture. The MAP considerably drops the handoff delay and signaling load for mobility management in IPv6. The Three Layered MIPv6 (TLMIPv6), the focus of this paper, is influenced by the benefits of placing MAPs for efficient mobility management. In this model, three MAPs are placed in the architecture to reduce signaling cost and handoff latency. These MAPs are placed hierarchically in a tree like architecture, and the movement of nodes is coordinated by different MAP based on nodes’ movement patterns. The behavior of the proposed model is simulated under various traffic scenarios and mobility conditions and compared with MIPv6 and HMIPv6 and Flow Based Distributed Mobility Management (FBDM) protocols. The results depict that when users are less mobile or confined their movements to a small geographical area, the proposed TLMIPv6 outperforms MIPv6, HMIPv6, and FBDM in handoff latency and signaling costs. Some suitable application scenarios for adopting TLMIPv6 are also mentioned at the end of the paper. The future scopes of the work are outlined.

Optimized seamless handover scheme with information and decision server‐assisted partially distributed mobility management in heterogeneous 5th generation wireless networks

SummaryExisting mobile network architectures are flatter and employ a centralized mobility management (CMM) scheme, where the entire intelligence is focused on the single‐end, which leads to low scalability, lack of dynamic mobility support, suboptimal routing, and insufficiency to support large volumes of data. The fifth‐generation (5G) networks demand high‐speed seamless connections to the best‐connected network irrespective of user position and connectivity. Further, seamless handover has to be carried out between wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX), universal mobile telecommunication system (UMTS), and long‐term evolution (LTE). This paper aims to propose a distributed mobility management (DMM) solution in order to bring the mobility anchor point nearer to the mobile node (MN) to support the acceptable quality of service (QoS) and quality of experience (QoE) provision to end users. An information and decision server (IDS)‐based partial DMM scheme (IDS‐PDMM) is suggested to support optimized handover in internet protocol (IP)‐based heterogeneous architecture. The IDS estimates the nearest candidate network for MN during handover and thus provides a seamless connection for delay‐sensitive applications. The performance of the presented approach is compared with the existing CMM scheme and the simulation results prove that the IDS‐PDMM efficiently minimizes the handover delay and packet loss rate with guaranteed QoS provision.

Mobility Management in Heterogeneous Cellular Networks

This paper has analysed the mobility management schemes in heterogenous cellular networks (HCNs) considering their power consumptions and network selection delays. The HCNs are the key enabler for 5G cellular network to ensure better user connectivity in ultra-dense networks. Large numbers of small cells are deployed under a macro-cell targeting network hot spot. However, ensuring seamless coverage to the mobile subscribers in motion is challenging in such networks. Frequent handoff between the cells with different bandwidth, throughput, latency, and coverage penetrations, significantly increases the signalling overhead in network. In this context, an efficient mobility management scheme plays a key role for the success of HCN technology. In this paper, a detailed behavioural study of mobility management schemes used in HCN has been provided. For mobile users in the network, the power consumption and network selection delay has been evaluated for k-tier architecture of HCN.

Attacks and failures prediction framework for a collaborative 5G mobile network

Although mobile technologies keep evolving through years, Fault management and cyber-security management in mobile networks are still treated as separated notions with different blocks and different approaches whereas in practice, they are highly correlated. In this paper, we propose a framework that takes into account the correlation between these two management systems. The framework is based on several prediction agents where each agent is composed of a security predictor, a fault predictor and a generic anomaly detection model. A re-enforcement process allows to enhance the reliability of the machine learning training and prediction phases of the different predictors. Besides, each agent can collaborate with its neighborhood for a more resilient network. An application of this framework to 5G architecture is proposed by mapping the components of our framework with network slices. Finally, an experimentation is held over a testbed that we set up on openstack in order to forecast future anomalies related to proxy overload, latency violation in call session network functions and to excessive usage of memory. The training is achieved with ARIMA and deep learning models with promising results.

Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G.

A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks

Mobility robustness optimization (MRO) is a fundamental issue regarding self-optimization network (SON). In long-term evolution-advanced (LTE-A), handover (HO) optimization in heterogeneous networks (HetNets) is an urgent need to improve system performance. This improvement is in terms of immunity against unnecessary HO (UHO) such as ping-pong HO (PPHO) and against failed HOs in the sense of radio link failure (RLF) such as too-early HO (TEHO) and too-late HO (TLHO), that is, RLF HO. The occurrence of these undesired HOs increases the consumption of network resources and decreases the quality of service (QoS). In this study, we propose a robust algorithm to reduce the number of PPHO, TLHO, and TEHO events to a minimum by an innovative mechanism that adaptively sets the HO control parameters (HCPs). This reduction is obtained without the need for unjustified techniques that assume certain thresholds for the ratio of PPHOs or the ratio of RLF HOs relative to the total number of HOs, as proposed in the most recent literature. We also present the importance of thinking about the existence of a direct relationship between the hysteresis margin (HM) and time-to-trigger (TTT). We invest this relationship in determining adaptive HCPs. Simulation results show that RLF HOs and PPHOs are minimal, almost zero, compared to the literature and the classical method. This study opens a new avenue for research on mobility management. It reorients research axes toward thinking about the possibility of a correlation between HM and TTT. This is what the research community has not yet realized.

On the Use of Composite Indicators for Mobile Communications Network Management in Smart Sustainable Cities

Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network’s performance. In this paper, we propose the use of composite indicators based on key performance indicators both as a tool for a cognitive management of mobile communications networks, as well as a metric which could successfully integrate more advanced user-centric measurements. Composite indicators can successfully synthesize and integrate large amounts of data, incorporating in a single index different metrics selected as triggers for autonomous decisions. The paper motivates and describes the use of this methodology, which is applied successfully in other areas with the aim of ranking metrics to simplify complex realities. A use case that is based on a universal mobile telecommunications system network is analyzed, due to technology simplicity and scalability, as well as the availability of key performance indicators. The use case focuses on analyzing the fairness of a network over different coverage areas as a fundamental metric in the operation and management of the networks. To this end, several ranking and visualization strategies are presented, providing examples of how to extract insights from the proposed composite indicator.