Heterogeneous Mobile Networks Research Articles

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.

Computer Vision-Based Risk Assessment on Heterogeneous Mobile Network Operating Environments

Computer Vision-Based Risk Assessment on Heterogeneous Mobile Network Operating Environments

Performance evaluation of handover triggering condition estimation using mobility models in heterogeneous mobile networks

AbstractHeterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi‐Fi, Zigbee, and Z‐wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi‐Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model.

Hybrid radio-over-fiber transport system to support heterogeneous indoor mobile network environments

The recent advances in mobile network technologies are based on pioneering higher radio frequency ranges such as millimeter wave and (sub-)terahertz bands. Most likely, the simultaneous use of multiple bands of such high frequency as well as mid/low frequency ranges should be considered to implement heterogeneous mobile environments where diverse conventional and emerging applications can coexist. This study aims to propose, demonstrate, and evaluate a radio-over-fiber (RoF)-based optical transport system that meets the aforementioned future indoor network’s requirements. In particular, we suggest a novel distributed antenna system architecture that can support next-generation mobile services such as millimeter wave 5G and terahertz 6G as well as commercial 5G systems employing low/mid bands. Following the experimental validation of the downlink and uplink transmission of radio signals, we investigate the cost reductions obtained by implementing the proposed system in comparison to conventional topologies such as point-to-point and wavelength division multiplexing-based point-to-multipoint connections.

QoE Aware Video Streaming Scheme Utilizing GRU-Based Bandwidth Prediction and Adaptive Bitrate Selection for Heterogeneous Mobile Networks

QoE Aware Video Streaming Scheme Utilizing GRU-Based Bandwidth Prediction and Adaptive Bitrate Selection for Heterogeneous Mobile Networks

Optimal Handover Optimization in Future Mobile Heterogeneous Network Using Integrated Weighted and Fuzzy Logic Models

Optimal Handover Optimization in Future Mobile Heterogeneous Network Using Integrated Weighted and Fuzzy Logic Models

Joint user plane function instance and base station scheduling in mobile networks

AbstractTo guarantee a high data transmission rate in heterogeneous mobile networks, sufficient small base stations (SBSs) and user plane function (UPF) instances should be active. However, the excessive operation of SBSs and UPF instances can increase the operating expenditure (OPEX) for the network operator. To balance the data rate and OPEX, we propose a joint UPF instance–SBS scheduling algorithm (J‐UBSA). In the proposed J‐UBSA, a controller periodically determines the appropriate number of active SBSs and UPF instances based on estimated variations in session requests. The decision‐making process is formulated as a constrained Markov decision process and converted into a linear programming model to obtain the optimal solution using a traditional algorithm with low complexity. Evaluation results demonstrate that J‐UBSA can substantially reduce the OPEX while guaranteeing a suitable average data rate for mobile devices.

Interference Management with Handover Performance in Heterogeneous Mobile Network

Interference Management with Handover Performance in Heterogeneous Mobile Network

Enhancing heterogeneous mobile network management based on a well-founded reference ontology

The fulfillment of the Always Best Connected & Served concept in future mobile wireless networks depends on their ability to maintain a seamless association between the user’s equipment and the network. As a result, many researchers and developers have proposed several decision-support mechanisms to cope with this challenge. One of the promising mechanisms to aid mobile wireless networks in achieving ubiquitous coverage with a seamless connection is semantic reasoning. However, once it relies on ontologies, proper representation of Link and Connection – entities that bind the communication nodes – is paramount. Unfortunately, although several network-related ontologies present these concepts, they are unclear on which entities they bind, nor do they present them simultaneously. Also, the same ambiguity can be perceived in vocabulary recommendations of several telecommunication standard bodies. Our main contribution is the ontological analysis that clarifies the definition of Link and Connection in telecommunication domain. Besides, we analyze their dependencies while refining other concepts such as Medium, Server, and Neighbor. Thus, we provide the foundations for a new network-related ontology to support mobility management in wireless networks.

Quality of service management method in а heterogeneous wireless network using Big Data technology аnd mobile QoE application

Today, with the increasing number of business users and their changing Quality of Service (QoS) requirements, there is a problem for mobile network operators to provide individual acceptable Quality of Experience (QoE) to their customers due to the lack of flexibility, agility, and scalability of traditional networks. For this reason, the latest scientific works are increasingly focusing on resource management, considering users' QoE, replacing the traditional system-oriented approach based on QoS. However, this problem has not been fully solved so far, as there are no approaches that take into account the user's intentions regarding the individualization of the specific types of services at a particular time for heterogeneous mobile networks. Thus, the development of means of feedback between the user and the operator on the adaptation of the QoE level or their intentions under changing user requirements is one of the important and unresolved tasks for future networks at the moment. A conceptual model for the construction of an Intent-Based Software-Defined Heterogeneous Network (IBSDHN) is proposed. This model ensures efficient management of shared network resources, adapting to business users' changing QoS requirements. The prototype of a mobile and operator application for adaptive client-oriented service delivery in a heterogeneous wireless network is developed, which allows obtaining the ordered QoE based on the feedback between the user and the operator. A method for QoS management in a heterogeneous wireless network using Big Data technology and a unique mobile (QoE) application is proposed. In contrast to the known, this method analyzes ordered QoE scores of service from users and searches for the optimal radio access network that meets these requirements based on the predicted QoE values. The experimental study proved that using the proposed method in a heterogeneous wireless network would reduce the number of dissatisfied users with the QoS up to 60%.

DOFMS: DRL-based Out-of-order Friendly Multipath Scheduling in Mobile Heterogeneous Networks

DOFMS: DRL-based Out-of-order Friendly Multipath Scheduling in Mobile Heterogeneous Networks

Enhancing Heterogeneous Mobile Network Management Based on a Well-Founded Reference Ontology

Enhancing Heterogeneous Mobile Network Management Based on a Well-Founded Reference Ontology

CONVERGENCE OF HETEROGENEOUS WIRELESS NETWORKS FOR FUTURE COMMUNICATIONS: ARCHITECTURE, QOS AND RESOURCE MANAGEMENT

Scientific and theoretical approaches to the implementation of a heterogeneous mobile communication network based on SDN/NFV and SDR technologies are described. The architecture of the future heterogeneous network is proposed, taking into account the evolution of emerging standards and key technologies. An algorithm for dynamic bandwidth allocation and reservation between several logical channels at a certain moment of time to provide QoS for information flows in future networks is created. A simulation model of network traffic service with parameters corresponding to real networks has been made. The study of femtocell SDR load, as the main convergent device at the level of heterogeneous network access by users of different generation mobile communication technologies has been conducted.

A Data-Driven Noninteractive Authentication Scheme for the Internet of Vehicles in Mobile Heterogeneous Networks.

The rapid development of intelligent vehicle networking technology has posed new requirements for in-vehicle gateway authentication security in the heterogeneous Internet of Vehicles (IoV). The current research on network layer authentication mechanisms usually relies on PKI infrastructure and interactive key agreement protocols, which have poor support for mobile and multihomed devices. Due to bandwidth and interaction delay overheads, they are not suitable for heterogeneous IoV scenarios with network state fluctuations. In this study, we propose a data-driven noninteractive authentication scheme, a lightweight, stateless scheme supporting mobility and multihoming to meet the lightweight data security requirements of the IoV. Our scheme implements device authentication and noninteractive key agreement through context parameters during data communication. Due to saving the signaling interactive delay and certificate overhead, in the IoV scenario, the proposed scheme reduced the delay by 20.1% and 11.8%, respectively, in the authentication and handover processes and brought higher bandwidth aggregation efficiency.

Risk Area Alert Through Heterogeneous Mobile Networks: A New Approach to Fight COVID-19 and Beyond

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this article, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID-19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end.

AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks

Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet). From both the environmental and economical perspectives, non-homogeneous QoS demands obstruct the minimization of the energy footprints and operational costs of the envisioned robust networks. As such, network intelligentization is expected to play an essential role in the realization of such sophisticated aims. The fusion of artificial intelligence (AI) and mobile networks will allow for the dynamic and automatic configuration of network functionalities. Machine learning (ML), one of the backbones of AI, will be instrumental in forecasting changes in network loads and resource utilization, estimating channel conditions, optimizing network slicing, and enhancing security and encryption. However, it is well known that ML tasks themselves incur massive computational burdens and energy costs. To overcome such obstacles, we propose a novel layer-based HetNet architecture which optimally distributes tasks associated with different ML approaches across network layers and entities; such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency via collaborative learning and communications.

Enhancing the Handover Performance in the Mobile Heterogeneous Network Based on the Expected Zone of Handover Events

This paper aims to enhance the handover performance in the heterogeneous networks (HetNet)by reducing the need to make handover under the specified conditions and also reducing the handover failure, thisstudy divided the target cell for ten assumed circular zones, where zones start with radius Rh=R/10 from center of target cell, then the next zone is increased by R/10, then the simulation is carried out for each assumed zone usingselective values of TTT (160,480 and 640 ms) in order to determine the best circular zone of more handover events (zone of handover) in the coverage area for the target cell and other zone for no handover events (zone of no handover).The paper found that the most practically suitable zone for more handover events was the zone which its radius represents 86.6% of the radius of the coverage area for the target cell. Whereas, the remained zone (the zone between the zone of handover and the edge of target cell is considered a zone for no handover events. Also, the angle of chord (Ѳ) for the expected zone of handover events from the horizontal axis is 60˚, i.e. if the UE moves bycertain velocity and with a moving angle less than or equal to 60˚, then it will have a large probability to make handover events after satisfying the other handover conditions. Otherwise, its probability to make handover events is very low. The paper extracted that after determining more suitable handover zone and implementing the different fixed and chosen values of TTT, then a remarkable enhancement occurred in the handover performance and it was obvious at high speed of travelling for the user.

A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems.

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

A novel heuristic for handover priority in mobile heterogeneous networks based on a multimodule Takagi–Sugeno–Kang fuzzy system

H2RDC (heuristic handover based on RCC-DTSK-C), a heuristic algorithm based on a highly interpretable deep Takagi–Sugeno–Kang fuzzy classifier, is proposed for suppressing the mobile heterogeneous networks problem of frequent handover and handover ping-pong in the multibase-station scenario. This classifier uses a stack structure between subsystems to form a deep classifier before generating a base station (BS) priority sequence during the handover process, and adaptive handover hysteresis is calculated. Simulation results show that H2RDC allows user equipment to switch to the best antenna at the optimal time. In high-BS density load and mobility scenarios, the proposed algorithm's handover success rate is similar to those of classic algorithms such as best connection (BC), self tuning handover algorithm (STHA), and heuristic for handover based on AHP-TOPSIS-FUZZY (H2ATF). Moreover, the handover rate is 83% lower under H2RDC than under BC, whereas the handover ping-pong rate is 76% lower.