scholarly journals Framework towards the Process of Estimating or Predicting Perceived QoE Based on the Datasets Obtained the Mobile Network

2019 ◽  
Vol 9 (2) ◽  
pp. 3627-3631
Keyword(s):  
Artificial Intelligence ◽  
Consumer Satisfaction ◽  
Quality Of Experience ◽  
Research Study ◽  
Mobile Network ◽  
Mobile Internet ◽  
Software Defined Network ◽  
Network Functionality ◽  
Mobile Network Operators

Nowadays, the research study community visualizes a standard shift that is going to put the focus on Quality of Experience metrics, which relate directly to complete consumer satisfaction. Yet, determining QoE coming from QoS sizes is a daunting job that powerful Software Defined Network operators are currently able to tackle through artificial intelligence strategies. In this paper, our experts pay attention to a few essential QoE factors, and we to begin with proposing a Bayesian Network design to anticipate re-buffering proportion. This paper suggested a structure for modeling mobile network QoE, making use of the vast records analytics approach. The planned platform explains the method of estimating or forecasting perceived QoE based upon the datasets obtained or collected from the mobile network to enable the mobile network operators efficiently to deal with the network functionality as well as supply the individuals an adequate mobile Internet QoE.

scholarly journals FRAMEWORK FOR MODELLING MOBILE NETWORK QUALITY OF EXPERIENCE THROUGH BIG DATA ANALYTICS APPROACH

2017 ◽  
Author(s):  
Ayisat Wuraola Yusuf-Asaju ◽  
Zulkhairi Md. Dahalin ◽  
Azman Ta’a
Keyword(s):  
User Experience ◽  
Data Analytics ◽  
Network Performance ◽  
Laboratory Experiments ◽  
Quality Of Experience ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Mobile Internet ◽  
Mobile Network Operators

The increase in the usage of different mobile internet applications can cause deterioration in the mobile network performance. Such deterioration often declines the performance of the mobile network services that can influence the mobile Internet user’s experience, which can make the internet users switch between different mobile network operators to get good user experience. In this case, the success of mobile network operators primarily depends on the ability to ensure good quality of experience (QoE), which is a measure of users’ perceived quality of mobile Internet service. Traditionally, QoE is usually examined in laboratory experiments to enable a fixed contextual factor among the participants even though the results derived from these laboratory experiments presented an estimated mean opinion score representing perceived QoE. The use of user experience dataset involving time and location gathered from the mobile network traffic for modelling perceived QoE is still limited in the literature. The mobile Internet user experience dataset involving the time and location constituted in the mobile network can be used by the mobile network operators to make data-driven decisions to deal with disruptions observed in the network performance and provide an optimal solution based on the insights derived from the user experience data. Therefore, this paper proposed a framework for modelling mobile network QoE using the big data analytics approach. The proposed framework describes the process of estimating or predicting perceived QoE based on the datasets obtained or gathered from the mobile network to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile Internet QoE.  

scholarly journals Predicting the Quality of Service Experienced By the Users Based on the Measurement History of the Network

2019 ◽  
Vol 9 (2) ◽  
pp. 2286-2290
Keyword(s):  
Mobile Phone ◽  
Network Performance ◽  
Mobile Network ◽  
Mobile Internet ◽  
Mobile Phone Network ◽  
History Of ◽  
Set Up ◽  
Mobile Network Operators ◽  
Internet Company

Growing from one generation to the following, wireless networks have frequently been enhancing their efficiency in various methods and also for varied purposes. The increase in the usage of different mobile phone world broad web functions may result in degeneration in the mobile network performance. Such destruction often drops the efficiency of the mobile network solutions that can influence the mobile World broad web consumer's experience, which can create the world wide web individuals switch between different mobile network operators to get excellent customer experience. In this particular situation, the effectiveness of mobile phone network operators primarily relies on the capability to make a certain top quality of experience (QoE), which is a procedure of consumers' identified quality of mobile Internet company. The goal is actually to exploit the information made by and already accessible in the network to appropriately release, set up, and optimize network nodules.

Toward Mobile Web 2.0-Based Business Methods

2011 ◽  
pp. 1515-1535
Author(s):  
Katarzyna Wac ◽  
Richard Bults ◽  
Bert-Jan van Beijnum ◽  
Hong Chen
Keyword(s):  
Service Providers ◽  
Mobile Network ◽  
Mobile Service ◽  
Mobile Web ◽  
Service Deployment ◽  
Business Cases ◽  
Novel Method ◽  
Assurance Process ◽  
Mobile Network Operators

Mobile service providers (MoSPs) emerge, driven by the ubiquitous availability of mobile devices and wireless communication infrastructures. MoSPs’ customers satisfaction and consequently their revenues, largely depend on the quality of service (QoS) provided by wireless network providers (WNPs) available at a particular location-time to support a mobile service delivery. This chapter presents a novel method for the MoSP’s QoS-assurance business process. The method incorporates a location- and time-based QoS-predictions’ service, facilitating the WNP’s selection. The authors explore different business cases for the service deployment. Particularly, they introduce and analyze business viability of QoSIS.net, an enterprise that can provide the QoS-predictions service to MoSPs, Mobile Network Operators (as MoSPs), or directly to their customers (i.e. in B2B/B2C settings). QoSIS.net provides its service based on collaborative-sharing of QoS-information by its users. The authors argue that this service can improve the MoSP’s QoS-assurance process and consequently may increase its revenues, while creating revenues for QoSIS.net.

OpenMobileNetwork

2013 ◽  
Vol 9 (4) ◽  
pp. 46-64 ◽  
Author(s):  
Abdulbaki Uzun ◽  
Eric Neidhardt ◽  
Axel Küpper
Keyword(s):  
Network Topology ◽  
Linked Data ◽  
Service Providers ◽  
Open Data ◽  
Mobile Network ◽  
Base Station ◽  
Network Provisioning ◽  
Crowdsourced Data ◽  
Mobile Network Operators

Mobile network operators maintain data about their mobile network topology, which is mainly used for network provisioning and planning purposes restricting its full business potential. Utilizing this data in combination with the extensive pool of semantically modeled data in the Linking Open Data Cloud, innovative applications can be realized that would establish network operators as service providers and enablers in the highly competitive services market. In this article, the authors introduce the OpenMobileNetwork (available at http://www.openmobilenetwork.org/) as an open solution for providing approximated network topology data based on the principles of Linked Data along with a business concept for network operators to exploit their valuable asset. Since the quality of the estimated network topology is crucial when providing services on top of it, the authors further analyze and evaluate state-of-the-art approaches for estimating base station positions out of crowdsourced data and discuss the results in comparison to real base station locations.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

scholarly journals KQI Performance Evaluation of 3GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1701
Author(s):  
Eduardo Baena ◽  
Sergio Fortes ◽  
Raquel Barco
Keyword(s):  
Quality Of Experience ◽  
Mobile Network ◽  
Service Performance ◽  
High Demand ◽  
Promising Candidate ◽  
Prioritization Method ◽  
Points Of View ◽  
5 Ghz ◽  
Traffic Prioritization

The rapid proliferation of user devices with access to mobile broadband has been a challenge from both the operation and deployment points of view. With the incorporation of new services with high demand for bandwidth such as video in 4K, it has been deemed necessary to expand the existing capacity by including new bands, among which the unlicensed 5-GHz band is a very promising candidate. The operation of future 3GPP (Third Generation Partnership Project) mobile network standards deployments in this band implies the coexistence with other technologies such as WiFi, which is widespread. In this context, the provision of Quality of Service (QoS) or Quality of Experience (QoE) becomes an essential asset and is a challenge that has yet to be overcome. In this sense, 3GPP has proposed a traffic prioritization method based on the Listen Before Talk access parameters, defining a series of priorities. However, it does not specify how to make use of them, and even less so in potentially conflicting situations. This paper assesses the end-to-end performance of downlink unlicensed channel priorities in dense scenarios via implementing a novel simulation setup in terms of both multi-service performance and coexistence.

Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 1739-1765
Author(s):  
Charalampos N. Pitas ◽  
Apostolos G. Fertis ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Mobile Communication ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Mobile Network ◽  
Mobile Users ◽  
Radio Access ◽  
Network Status

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems

2016 ◽  
pp. 512-535
Author(s):  
Charalampos N. Pitas ◽  
Apostolos G. Fertis ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Mobile Communication ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Mobile Network ◽  
Mobile Users ◽  
Radio Access ◽  
Network Status

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

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