Current Mobile Networks Research Articles

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Analyzing Mobility Patterns at Scale in Pandemic Scenarios Leveraging the Mobile Network Ecosystem

The ubiquity and pervasiveness of mobile network technologies has made them so deeply ingrained in our everyday lives that by interacting with them for very simple purposes (e.g., messaging or browsing the Internet), we produce an unprecedented amount of data that can be analyzed to understand our behavior. While this practice has been extensively adopted by telcos and big tech companies in the last few years, this condition, which was unimaginable just 20 years ago, has only been mildly exploited to fight the COVID-19 pandemic. In this paper, we discuss the possible alternatives that we could leverage in the current mobile network ecosystem to provide regulators and epidemiologists with the right understanding of our mobility patterns, to maximize the efficiency and extent of the introduced countermeasures. To validate our analysis, we dissect a fine-grained dataset of user positions in two major European countries severely hit by the pandemic. The potential of using these data, harvested employing traditional mobile network technologies, is unveiled through two exemplary cases that tackled macro and microscopic aspects.

Understanding Mobile OTT Service Users' Resistance to Participation in Wireless D2D Caching Networks.

With the explosive pace of mobile over-the-top (OTT) video content streaming services, mobile network traffic has seen unprecedented growth in recent years. However, the limitation of antenna performance, the burden of investment cost, and restricted resources hinder improving the current mobile networks' functionality. Accordingly, wireless device-to-device (D2D) caching networks came to the fore as one of the competitive alternatives for alleviating the overloads of mobile network traffic. Wireless D2D caching networks can be a desirable alternative for OTT service providers and telecommunication operators, but the problem is user resistance. User participation is imperative to deliver wireless D2D caching network functionality successfully. Thus, to gain a deeper understanding of user resistance toward wireless D2D caching networks and their underlying sources, this study introduces two perceived cost factors contributing to this resistance and one perceived benefit that mitigates such resistance. Based on an online survey, this study found new theoretical links among perceived costs and benefits, resistance, and participation intention. The findings reveal that user resistance is predicted by perceived costs, encompassing resource sacrifices and privacy concerns, whereas perceived benefits-specifically, perceived usefulness-did not significantly influence resistance. This implies that telecommunication operators should prioritize market requirements over technological advantages, emphasizing the potential for successful commercialization of wireless D2D caching networks.

Opportunities and Challenges of Tangible XR Applications for 5G Networks and Beyond

The increasing popularity of virtual reality (VR) and augmented reality (AR) applications has unveiled the need for boosting the efforts of implementing extended reality (XR) to users. An analogous high interest from both academia and industry is being shown for tactile internet (TI). The ever-increasing use of virtualization technologies makes the usage of haptic communication even more important in order to create robust tangible systems, but also to achieve an optimal and faster balance among the interaction modalities, as well as between humans and machines. The current wireless mobile networks cannot provide ultra-low latency, massive computational capability, and high communication bandwidth, requirements which are crucial for applications such as extended reality and tactile internet. A simplified tangible XR system is proposed accompanied with the opportunities and challenges of such an implementation.

LES ENJEUX DE LA MAINTENANCE DES RÉSEAUX DES OPÉRATEURS DE TÉLÉPHONIE MOBILE ENTRE OPERATEURS, PRESTATAIRE ET SOUS-TRAITANT, CAS DE HUAWEI ET ELMS EN RDC

The maintenance system of the current mobile phone networks is dominated by the subcontracting that passes for one of the organizations that relaxes the client or the operator and allows it to focus more favorably on essential tasks of production, hear by commercial and marketing. Thus outsourcing is organized according to established criteria for reconciling cost and quality, it is used to make a selection initially for recruitment that can satisfy the specifications. 
 In the DRC, the provider HUAWEI takes on a chance of dominance and treats with all the major mobile operators from the point of view maintenance and supplies of the equipment as it counts to its bosom several sub-portent, with whom it cooperates to organize a system of effective maintenance. 
 To make its vision practice, strategically its subcontractors are geographically retapled in the maintenance zone. The ELMS subcontractor is renowned for the ex-region of Ecuador where Vodacom, Orange and Airtel are important. In this article we identify the continuous possibilities of maintenance structure in mobile and technical telephone networks aimed at reducing maintenance costs related to maintenance while optimizing the reliability of the networks. The objective principle of this article is developed a technical orientation and reference technical document at different levels for the maintenance of mobile phone equipment in the DRC by giving information on a judicious choice of maintenance partners, based on efficiencies of efficiency. Emergence of reliable networks in networks in the DRC.

An overview of LTE/LTE‐A heterogeneous networks for 5G and beyond

AbstractThe potential of fifth‐generation (5G) and beyond wireless connectivity is to deliver higher data rates, much lower latency, more frequent increase in channel capacity, and significant improvements in Quality of Service (QoS), compared to the current Long Term Evolution (LTE) networks. The continued growth of smart devices, the introduction of trending multimedia applications, and the dramatic increase in demand and use of wireless data communication are already causing a significant load on current mobile networks. 5G wireless networks, with enhanced user data rates, delays, capacity and QoS, are anticipated to solve many of the existing problems of mobile networks. In this study, we review the emergent wireless LTE‐A and 5G networks to provide an overview of the challenges and the solutions suggested in recent literature towards forthcoming 6G networks. We first discuss the new structural changes associated with the Radio Access Network (RAN) design, heterogeneous network and LTE or LTE‐Advanced (LTE‐A) network. To understand the challenges and research gaps in 5G and beyond networks, the paper reviews the outstanding features of the new QoS and Self‐Organization Networks (SON) related to the emerging 5G networks. Data rates, bandwidth and coverage have been given significance throughout this review because these are some major challenges towards 6G networks. Since understanding the present state of 5G usage is critical to its adoption, the paper also discusses related field tests, trials, and simulation tests. Finally, we identify existing major research problems and outline potential research areas.

Mission Critical Communications Support With 5G and Network Slicing

Mission Critical (MC) communications take a pivotal role to achieve effective Public Protection and Disaster Relief (PPDR) actions. Even though 3GPP standards define MC applications and services in an architectural framework compatible with current 5G mobile networks, real-life experiments and applications of these concepts are still at the very beginning. In this paper, we present an architectural study and related experimental activity on network slicing for MC communications. We implemented these services in a fully virtualized environment, and deployed and tested them in a multi-domain network slicing scenario compliant with the ETSI NFV-MANO specifications. Our work aligns with the 5G approach separating control and data planes. The level of automation in service deployment and the slice isolation features are demonstrated, showing the benefits in terms of application performance, management flexibility, scalability, and quality of service differentiation capabilities.

EBPF: A New Approach to Cloud-Native Observability, Networking and Security for Current (5G) and Future Mobile Networks (6G and Beyond)

eBPF: A New Approach to Cloud-Native Observability, Networking and Security for Current (5G) and Future Mobile Networks (6G and Beyond)

Improved flat mobile core network architecture for 5G mobile communication systems

The current mobile network core is built based on a centralized architecture, including the S-GW and P-GW entities to serve as mobility anchors. Nevertheless, this architecture causes non-optimal routing and latency for control messages. In contrast, the fifth generation (5G) network will redesign the network service architecture to improve changeover management and deliver clients a better Quality-of-Experience (QoE). To enhance the design of the existing network, a distributed 5G core architecture is introduced in this study. The control and data planes are distinct, and the core network also combines IP functionality anchored in a multi-session gateway design. We also suggest a control node that will fully implement the control plane and result in a flat network design. Its architecture, therefore, improves data delivery, mobility, and attachment speed. The performance of the proposed architecture is validated by improved NS3 simulation to run several simulations, including attachment and inter- and intra-handover. According to experimental data, the suggested network is superior in terms of initial attachment, network delay, and changeover management.

LSTM-Based DWBA Prediction for Tactile Applications in Optical Access Network

Historically, the optical access network (OAN) plays a crucial role of supporting emerging new services such as 4 k, 8 k multimedia streaming, telesurgery, augmented reality (AR), and virtual reality (VR) applications in the context of Tactile Internet (TI). In order to prevent losing connectivity to the current mobile network and Tactile Internet, the OAN must expand capacity and improve the quality of Services (QoS) mainly for the low latency of 1 ms. The optical network has adopted artificial intelligence (AI) technology, such as deep learning (DL), in order to classify and predict complex data. This trend mainly focuses on bandwidth prediction. The software-defined network (SDN) and cloud technologies provide all the essential capabilities for deploying deep learning to enhance the performance of next-generation ethernet passive optical networks (NG-EPONs). Therefore, in this paper, we propose a deep learning long-short-term-memory model-based predictive dynamic wavelength bandwidth allocation (DWBA) mechanism, termed LSTM-DWBA in NG-EPON. Future bandwidth for the end-user is predicted based on NG-EPON MPCP control messages exchanged between the OLT and ONUs and cycle times. This proposed LSTM-DWBA addresses the uplink control message overhead and QoS bottleneck of such networks. Finally, the extensive simulation results show the packet delay, jitter, packet drop, and utilization.

QoE-Driven Distributed Resource Optimization for Mixed Reality in Dynamic TDD Systems

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

Smart data preprocessing method for remote vehicle diagnostics to increase data compression efficiency

The increasing number of functions in modern vehicle leads to an exponential increase in software complexity. The validity and reliability of all components must be ensured, making the use of appropriate vehicle diagnostics systems indispensable. The purpose of such systems is to collect and process data about the vehicle. To find issues during vehicle development, the OEMs will usually have a development fleet of thousands of vehicles. The challenge for diagnostic systems is to detect issues during these tests, as well as collecting as much data as possible about the circumstances that led to the fault. A single-vehicle produces hundreds of gigabytes of data per month. The required data bandwidth cannot be fulfilled by current mobile network subscriptions as well as WIFI or cable-based infrastructure. This limits the amount of data that can be collected during field tests and hinders big data analysis like AI training or validation. Hence a software solution for data reduction is necessary. The authors present a method for data handling that drastically reduces the amount of data consumption and optimizes the transfer delay between a remote-diagnostic systems and the cloud. Using a pipeline of data preprocessing as well as an established compression algorithm, the amount of transmitted data is reduced by a factor of nearly ten. This method will allow to collect more data in field testing and improve the understanding of issues during vehicle development.

Communication Challenges in Infrastructure-Vehicle Cooperative Autonomous Driving: A Field Deployment Perspective

We have commercially deployed an infrastructure-vehicle cooperative autonomous driving system and verified its superiority over on-vehicle only autonomous driving systems. However, we have also identified network communication as the main technical roadblock for reliable cooperative autonomous driving. Hence, the goal of this article is to help the communications research community to better understand the real-world communication challenges in cooperative autonomous driving, as well as to introduce practical solutions for establishing a good baseline for further research and development efforts. Specifically, we introduce the real-world network communication challenges: first, the current mobile network bandwidth is constraining the uploading of raw sensing data, which is crucial for cloud-based applications such as deep learning model training and high-definition map generation and so on. Second, the network latency jitters remain high for a considerable portion of a vehicle's trip, greatly impacting the reliability and safety of the operations of autonomous vehicles. To address these two challenges, we have developed, deployed, and verified two practical solutions. First, a sensing compression strategy to cope with the network bandwidth challenge. Second, an adaptive fusion engine to cope with the latency variation challenge.

Context-Aware Spectrum Sharing and Allocation for Multiuser-Based 5G Cellular Networks

The number of users of current mobile and cellular networks is constantly increasing. The allocation of spectrum to its users is constantly facing various hurdles. This causes users to leave that network and connect to another network. Thus, those telecom companies are constantly struggling to provide high-speed services to the users. As a result, the demand for 5G networks is currently increasing. Considering these, an algorithm has been proposed here to suit the needs of the users. Its main feature is that it easily identifies the primary and secondary users of the 5G network and creates a spectrum allocation system for them accordingly. Generally, all other methods are designed with the primary user in mind. Furthermore, the spectrum hole calculation that is currently being proposed is done accurately so that the spectrum switching processes required for the secondary user can take place here very quickly so that the secondary user can use the spectrum without any hindrance. The proposed model achieved 93.29% of spectrum blocking, 6.71% of spectrum band dropping, 94.03% bandwidth utilization, 1073 ms end-to-end delay, and 17273 bps of throughput. The proposed model effectively handles the spectrum and intelligent approach to resolve the spectrum hole problems. The existing models are practically faced with these problems. The proposed model spectrum utilization and efficiency were increased compared with the existing models.

Analog Radio Over Fiber Aided C-RAN: Optical Aided Beamforming for Multi-User Adaptive MIMO Design

Given the increasing demand for high data-rate, high-performance wireless communications services, the demand on the radio access networks (RAN) has been increasing significantly, where optical fiber has been widely used both for the backhaul and fronthaul. Additionally, advances in signal processing such as multiple-input multiple-output (MIMO) techniques, have improved the performance as well as transmission rate of communications networks. Beamforming has been used as an efficient MIMO technique for providing a signal to noise ratio (SNR) gain as well as reducing the multi-user interference. However, beamforming requires the employment of phase-shifters, which suffers from reduced phase resolutions, degraded noise figures as well as beam-squinting in addition to the implementation challenges. Hence, in this paper we employ an analogue radio over fiber (A-RoF) aided architecture for supporting the requirements of the current and future mobile networks, where we design a photonics aided beamforming technique in order to eliminate the bulky electronic phase-shifters and the beam-squinting effect, while also providing a low-cost RAN solution. Additionally, this photonics aided beamforming is combined with a reconfigurable multi-user MIMO technique, where users can communicate with one or multiple remote radio heads (RRHs), while employing stand-alone beamforming, beamforming combined with diversity or with multiplexing depending on the available resources and the user channel information as well as the quality of service requirements.

Statistical Model for Mobile User Positioning Based on Social Information

In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems pinpointing problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the scenario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from data traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged posts from social networks collected in real-time.

A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics

With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a wireless access node in a heterogeneous environment. A structural diagram of the optimization stages for wireless heterogeneous networks was developed, making it possible to improve the efficiency of their functioning. A model for studying the processes of functioning of a heterogeneous network environment is proposed. This model uses the methodology of big data evaluation to perform data transmission monitoring, analysis of tasks generated by network users, and statistical output of vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of the heterogeneous network is increased by 16% when using the method of static reservation of network resources, compared to homogeneous networks, and another 13% when using a uniform distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users.

Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Rainfall measurement from Commercial microwave links for urban hydrology in Africa: a simulation framework for sensitivity analysis.

Abstract Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou where a hydrological model has been set up. The analysis is based on numerical simulations, using high resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: i) the precision of the raw information provided by the operator and ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or short (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall.

The Development of Mobile Augmented Reality Application to Facilitate High School Students with Various Learning Strategies in Learning History

Augmented Reality is a demanding feature that widely embeds in our mobile devices. With the current mobile networks this feature will be increasingly accessible in many areas such as education. The current situation in learning history is boring and unattractive to high school students. In addition, the current history books as well as additional revision books are very textual and contain less pictures.The aim of this paper is to describe the mapping of the different learning styles that the students have employed onto the AR mobile application in learning history. The mapping should facilitate all learning styles so that learning history becomes more interactive and engaging.