Hasil untuk "Telecommunication"

M. Shalaev, W. Walasik, A. Tsukernik et al.

H. Ren, J. Jang, Chenhao Li et al.

Dispersion engineering is essential to the performance of most modern optical systems including fiber-optic devices. Even though the chromatic dispersion of a meter-scale single-mode fiber used for endoscopic applications is negligible, optical lenses located on the fiber end face for optical focusing and imaging suffer from strong chromatic aberration. Here we present the design and nanoprinting of a 3D achromatic diffractive metalens on the end face of a single-mode fiber, capable of performing achromatic and polarization-insensitive focusing across the entire near-infrared telecommunication wavelength band ranging from 1.25 to 1.65 µm. This represents the whole single-mode domain of commercially used fibers. The unlocked height degree of freedom in a 3D nanopillar meta-atom largely increases the upper bound of the time-bandwidth product of an achromatic metalens up to 21.34, leading to a wide group delay modulation range spanning from −8 to 14 fs. Furthermore, we demonstrate the use of our compact and flexible achromatic metafiber for fiber-optic confocal imaging, capable of creating in-focus sharp images under broadband light illumination. These results may unleash the full potential of fiber meta-optics for widespread applications including hyperspectral endoscopic imaging, femtosecond laser-assisted treatment, deep tissue imaging, wavelength-multiplexing fiber-optic communications, fiber sensing, and fiber lasers. The authors fabricate a 3D achromatic diffractive metalens on the end face of a single-mode fiber, useful for endoscopic applications. They demonstrate achromatic and polarization insensitive focusing across the entire near-infrared telecommunication wavelength band ranging from 1.25 to 1.65 µm.

Nour Mheidly, M. Fares, Jawad Fares

The COVID-19 pandemic substantially impacted the field of telecommunication. It increased the use of media applications that enable teleconferencing, telecommuting, online learning, and social relations. Prolonged time facing screens, tablets, and smart devices increases stress and anxiety. Mental health stressors associated with telecommunication can add to other stressors related to quarantine time and lockdown to eventually lead to exhaustion and burnout. In this review, the effects of the COVID-19 pandemic on communication and education are explored. In addition, the relationship between prolonged exposure to digital devices and mental health is studied. Finally, coping strategies are offered to help relieve the tele-burdens of pandemics.

Afshin Rashid

Yelong Zhang, Yanqi Zhao, Siyuan Dai et al.

C. Alenoghena, H. Ohize, A. Adejo et al.

The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review several articles on eHealth and Telemedicine with emphasis on the articles’ focus area, including wireless technologies and architectures in eHealth, communications protocols, Quality of Service, and Experience Standards, among other considerations. In addition, we provide an overview of telemedicine for new readers. This survey reviews several telecommunications technologies currently being proposed along with their standards and challenges. In general, an encompassing survey on the developments in telemedicine technology, standards, and protocols is presented while acquainting researchers with several open issues. Special mention of the state-of-the-art specialist application areas are presented. We conclude the survey paper by presenting important research challenges and potential future directions as they pertain to telemedicine technology.

Adnan Amin, Feras N. Al-Obeidat, B. Shah et al.

Customer Churn Prediction (CCP) is a challenging activity for decision makers and machine learning community because most of the time, churn and non-churn customers have resembling features. From different experiments on customer churn and related data, it can be seen that a classifier shows different accuracy levels for different zones of a dataset. In such situations, a correlation can easily be observed in the level of classifier's accuracy and certainty of its prediction. If a mechanism can be defined to estimate the classifier's certainty for different zones within the data, then the expected classifier's accuracy can be estimated even before the classification. In this paper, a novel CCP approach is presented based on the above concept of classifier's certainty estimation using distance factor. The dataset is grouped into different zones based on the distance factor which are then divided into two categories as; (i) data with high certainty, and (ii) data with low certainty, for predicting customers exhibiting Churn and Non-churn behavior. Using different state-of-the-art evaluation measures (e.g., accuracy, f-measure, precision and recall) on different publicly available the Telecommunication Industry (TCI) datasets show that (i) the distance factor is strongly co-related with the certainty of the classifier, and (ii) the classifier obtained high accuracy in the zone with greater distance factor's value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor's value (i.e., customer churn and non-churn with low certainty).

Lewlisa Saha, Hrudaya Kumar Tripathy, T. Gaber et al.

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively.

Wei Hu, H. Cong, Wei Huang et al.

A high-performance and broadband heterojunction photodetector has been successfully fabricated. The heterostructure device is based on a uniform and pinhole-free perovskite film constructed on top of a single-crystal germanium layer. The perovskite/germanium photodetector shows enhanced performance and a broad spectrum compared with the single-material-based device. The photon response properties are characterized in detail from the visible to near-infrared spectrum. At an optical fibre communication wavelength of 1550 nm, the heterojunction device exhibits the highest responsivity of 1.4 A/W. The performance is promoted because of an antireflection perovskite coating, the thickness of which is optimized to 150 nm at the telecommunication band. At a visible light wavelength of 680 nm, the device shows outstanding responsivity and detectivity of 228 A/W and 1.6 × 1010 Jones, respectively. These excellent properties arise from the photoconductive gain boost in the heterostructure device. The presented heterojunction photodetector provides a competitive approach for wide-spectrum photodetection from visible to optical communication areas. Based on the distinguished capacity of light detection and harvesting from the visible to near-infrared spectrum, the designed germanium/perovskite heterostructure configuration is believed to provide new building blocks for novel optoelectronic devices. A device made from germanium and perovskite layers can detect light in the visible and near-infrared ranges, showing potential for use in a wide range of applications, including in optical communications and next-generation optoelectronics. This heterojunction photodetector fabricated by Chunlai Xue of the Chinese Academy of Sciences and colleagues overcomes problems in single-material photodetectors, which are unable to detect a broad range of light. Recent research into various combinations of semiconducting materials for heterojunction photodetectors has led to devices with poor sensitivity to light or that require a high working voltage. Adding a layer of methylammonium lead triiodide perovskite to a layer of germanium resulted in a highly sensitive photodetector at the optical fibre communication wavelength of 1550 nm (near-infrared range) and the visible light wavelength of 680 nm.

Yiming Zhang, Yuxi Liu, Sailing He

ABSTRACT A compact leaky‐wave antenna (LWA) with innovative phase‐shift asymmetric coupling for continuous beam scanning is presented. The antenna utilises a slow‐wave half‐mode substrate integrated waveguide with spoof surface plasmon polaritons (SW‐HMSIW‐SSPP) transmission line structure to achieve ultra‐compact dimensions in both longitudinal and lateral directions. The radiation characteristic is achieved using sinusoidal modulation on the SSPP structure. To enable continuous beam scanning through broadside, a novel and simple phase‐shift asymmetric coupling method is developed by placing sinusoidally modulated patches with π/2 phase shift on the metallised blind via‐hole arrays. This approach effectively suppresses the open stopband (OSB) and enables continuous beam scanning from backward to forward directions without radiation degradation at broadside. A prototype of the proposed LWA is fabricated and characterised. The measured results demonstrate that the antenna with 12 unit‐cells operates over a wide frequency range from 14.3 to 20.5 GHz with continuous beam scanning from −40° to +30°, while maintaining an ultra‐compact aperture of only 6.67 λ0 × 0.27 λ0.

Zi Wang, Lorry Chang, Feifan Wang et al.

Miniaturized image classifiers are potential for revolutionizing their applications in optical communication, autonomous vehicles, and healthcare. With subwavelength structure enabled directional diffraction and dispersion engineering, the light propagation through multi-layer metasurfaces achieves wavelength-selective image recognitions on a silicon photonic platform at telecommunication wavelength. The metasystems implement high-throughput vector-by-matrix multiplications, enabled by near 103 nanoscale phase shifters as weight elements within 0.135 mm2 footprints. The diffraction manifested computing capability incorporates the fabrication and measurement related phase fluctuations, and thus the pre-trained metasystem can handle uncertainties in inputs without post-tuning. Here we demonstrate three functional metasystems: a 15-pixel spatial pattern classifier that reaches near 90% accuracy with femtosecond inputs, a multi-channel wavelength demultiplexer, and a hyperspectral image classifier. The diffractive metasystem provides an alternative machine learning architecture for photonic integrated circuits, with densely integrated phase shifters, spatially multiplexed throughput, and data processing capabilities. Metasystem architectures are attractive alternatives to waveguide-based integrated photonic processors due to the subwavelength structures. Here, the authors report a 1D passive silicon photonic metasystem with near 90% spatial pattern classification accuracy at telecommunication wavelength.

Mousa Alizadeh, Mohammad T. H. Beheshti, Amin Ramezani et al.

With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

Ruoyu Su, Deng-yin Zhang, R. Venkatesan et al.

With the rapid and sustained growth of network demands, 5G telecommunication networks are expected to provide flexible, scalable, and resilient communication and network services, not only for traditional network operators, but also for vertical industries, OTT, and third parties to satisfy their different requirements. Network slicing is a promising technology to establish customized end-to-end logic networks comprising dedicated and shared resources. By leveraging SDN and NFV, network slices associated with resources can be tailored to satisfy diverse QoS and SLA. Resource allocation of network slicing plays a pivotal role in load balancing, resource utilization, and networking performance. In this article, we focus on the principles and models of resource allocation algorithms in 5G network slicing. We first introduce the basic ideas of the SDN and NFV with their roles in network slicing. The MO architecture of network slicing is also studied, which provides a fundamental framework of resource allocation algorithms. Then, resource types with corresponding isolation levels in RAN slicing and CN slicing are analyzed, respectively. Furthermore, we categorize the mathematical models of resource allocation algorithms based on their objectives and elaborate them with typical examples. Finally, open research issues are identified with potential solutions.

Hemlata Jain, A. Khunteta, S. Srivastava

Abstract Today in every industry weather, it is ISP, IT products, social network or mobile services there is the problem of customer churn (Customers changing their services from one service provider to another). However, in telecommunication the customers churning very frequently. As the market in telecom is fiercely competitive, in that case, companies proactively have to determine the customers churn by analyzing their behavior and try to put effort and money in retaining the customers. In this proposed model, two machine-learning techniques were used for predicting customer churn Logistic regression and Logit Boost. Experiment was carried out in the WEKA Machine-learning tool, along with a real database from an American company Orange. The result were shown in different evaluation measures.

Mac Junior Abeka, Eric Andoh, J. Gatsi et al.

Abstract The economic growth of most sub-Saharan African countries in the past years has not been able to equalize with other regions. Even though financial development has been highlighted in several empirical literature as a factor that could spur up economic growth, the level of financial development in sub-Saharan Africa is not effectively channeled into desired levels of economic growth. However, there is an indication in the literature that financial development will be more relevant to the economic growth of sub-Saharan African economies that have strong telecommunication infrastructure. Using the system General Method of Moment estimation technique, the paper found that telecommunication infrastructure enhances the effect of financial development on the economic growth of sub-Saharan African economies. It is therefore recommended that sub-Saharan African economies should apply appropriate measures to boost their telecommunication infrastructure so that gains from the financial sector can effectively be channeled into economic growth.

B. Da Lio, C. Faurby, Xiaoyan Zhou et al.

On‐demand single‐photon sources emitting pure and indistinguishable photons at the telecommunication wavelength are critical assets toward the deployment of fiber‐based quantum networks. Indeed, single photons may serve as flying qubits, allowing communication of quantum information over long distances. Self‐assembled InAs quantum dots embedded in GaAs constitute an excellent nearly deterministic source of high‐quality single photons, but the vast majority of sources operate in the 900–950 nm wavelength range, precluding their adoption in a quantum network. A quantum frequency conversion scheme is presented here for converting single photons from quantum dots to the telecommunication C band, around 1550 nm, achieving 40.8% end‐to‐end efficiency, while maintaining both high purity and a high degree of indistinguishability during conversion with measured values of g(2)(0)=2.4%$g^{(2)}(0)=2.4\%$ and V(corr)=94.8%$V^{\mbox{(corr)}}=94.8\%$ , respectively.

Mubarak Yakubova, Olga Manankova, Assel Mukasheva et al.

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping.

Choonkill Park, M. Khater, A. Abdel‐Aty et al.

Abstract In this paper, a nonlinear fractional emerging telecommunication model with higher–order dispersive cubic–quintic is studied by using two recent computational schemes. This kind of model is arising in many applications such as machine learning and deep learning, cloud computing, data science, dense sensor network, artificial intelligence convergence, integration of Internet of Things, self–service IT for business users, self-powered data centers, and dense sensor networks (DSNs) that is used in the turbine blades monitoring and health monitoring. Two practical algorithms (modified Khater method and sech–tanh functions method) are applied to higher–order dispersive cubic–quintic nonlinear complex fractional Schrodinger ( NLCFS ) equation. Many novel traveling wave solutions are constructed that do not exist earlier. These solutions are considered as the icon key in the emerging telecommunication field, were they able to explain the physical nature of the waves spread, especially in the dispersive medium. For more illustration, some attractive sketches are also depicted for the interpretation physically of the achieved solutions.

I. Pustokhina, D. Pustokhin, P. T. Nguyen et al.

Customer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

Joel Persson, J. Parie, S. Feuerriegel

Significance To manage the current epidemic, policymakers need tools that help them in evidence-based decision making. In particular, decision support is needed to assess policy measures by their ability to enforce social distancing. A solution is offered by our work: We use mobility data derived from telecommunication metadata as a proxy for social distancing, and, based on this, we demonstrate how the effect of policy measures can be monitored in a nationwide setting. Compared to the status quo, this provides a clear benefit: Monitoring policy measures through case counts has a substantial time lag, whereas our approach allows for monitoring in near real time. In response to the novel coronavirus disease (COVID-19), governments have introduced severe policy measures with substantial effects on human behavior. Here, we perform a large-scale, spatiotemporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; 10 February to 26 April 2020), consisting of ∼1.5 billion trips. In comparison to the same time period from 2019, human movement in Switzerland dropped by 49.1%. The strongest reduction is linked to bans on gatherings of more than five people, which are estimated to have decreased mobility by 24.9%, followed by venue closures (stores, restaurants, and bars) and school closures. As such, human mobility at a given day predicts reported cases 7 to 13 d ahead. A 1% reduction in human mobility predicts a 0.88 to 1.11% reduction in daily reported COVID-19 cases. When managing epidemics, monitoring human mobility via telecommunication data can support public decision makers in two ways. First, it helps in assessing policy impact; second, it provides a scalable tool for near real-time epidemic surveillance, thereby enabling evidence-based policies.