scholarly journals Self-Organized Cell Outage Detection Architecture and Approach for 5G H-CRAN

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Yu ◽  
Fanqin Zhou ◽  
Tao Zhang ◽  
Wenjing Li ◽  
Lei Feng ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Negative Impact ◽  
Use Case ◽  
5G Networks ◽  
Self Organized ◽  
Radio Access ◽  
Detection Approach ◽  
High Bandwidth ◽  
Heterogeneous Cloud

An attractive architecture called heterogeneous cloud radio access networks (H-CRAN) becomes one of the important components of 5G networks, which can provide ubiquitous high-bandwidth services with flexible network construction. However, massive access nodes increase the risk of cell outages, leading to negative impact on user-perceived QoS (Quality of Service) and QoE (Quality of Experience). Thus, cell outage management (COM) became a key function proposed in SON (Self-Organized Networks) use cases. Based on COM, cell outage detection (COD) will be resolved before cell outage compensation (COC). Currently few studies concentrate on COD for 5G H-CRAN, and we propose self-organized COD architecture and approach for it. We firstly summarize current COD solutions for LTE/LTE-A HetNets and then introduce self-organized architecture and approach suitable for H-CRAN, which includes COD architecture and procedures, and corresponding key technologies for it. Based on the architecture, we take a use case with handover data analysis using modified LOF (Local Outlier Factor) detection approach to detect outage for different kinds of cells in H-CRAN. Results show that the proposed approach can identify the outage cell effectively.

scholarly journals Obtaining More Realistic Cross-Layer QoS Measurements: A VoIP over LTE Use Case

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
F. Javier Rivas ◽  
Almudena Díaz ◽  
Pedro Merino
Keyword(s):  
Quality Of Experience ◽  
Access Network ◽  
Added Value ◽  
Cross Layer ◽  
Use Case ◽  
Lte Networks ◽  
Testing Environment ◽  
Radio Access ◽  
Open Issue

We introduce a real-time experimentation testbed in this paper which enables more realistic analysis of quality of service (QoS) in LTE networks. This testbed is envisioned for the improvement of QoS and quality of experience (QoE) through the experimentation with real devices, services, and radio configurations. Radio configurations suggested in the literature typically arise from simulations; the testbed provides a real and controlled testing environment where such configurations can be validated. The added value of this testbed goes a long way not only in the provision of more realistic results but also in the provision of QoS and QoE cross-layer measurements through the correlation of information collected at different layers: from service and IP levels to radio and protocol parameters. Analyzing the interlayer dependencies will allow us to identify optimal settings for the radio access network and service parameters. This information can be used to suggest new cross-layer optimizations to further improve quality of experience of mobile subscribers. As a use case, we examine VoIP service over LTE, which is currently an open issue.

scholarly journals Quality of experience aware network selection model for service provisioning in heterogeneous network

2022 ◽  
Vol 12 (2) ◽  
pp. 1839
Author(s):  
Nagaraja Gadde ◽  
Basavaraj Jakkali ◽  
Ramesh Babu Halasinanagenahalli Siddamallaih ◽  
Gowrishankar Gowrishankar
Keyword(s):  
Quality Of Experience ◽  
Heterogeneous Wireless Networks ◽  
Packet Transmission ◽  
User Preference ◽  
Performance Criteria ◽  
Research Issues ◽  
Radio Access ◽  
Multi Mode ◽  
Rat Selection

Heterogeneous wireless networks (HWNs) are capable of integrating the different radio access technologies that make it possible to connect mobile users based on the performance parameters. Further quality of service (QoS) is one of the major topics for HWNs, moreover existing radio access technology (RAT) methodology are designed to provide network QoS criteria. However, limited work has been carried out for the RAT selection mechanism considering user QoS preference and existing models are developed based on the multi-mode terminal under a given minimal density network. For overcoming research issues this paper present quality of experience (QoE) RAT (QOE-RAT) selection methodology, incorporating both network performance criteria and user preference considering multiple call and multi-mode HWNs environment. First, this paper presents fuzzy preference aware weight (FPAW) and multi-mode terminal preference aware TOPSIS (MMTPA-TOPSIS) for choosing the best RAT for gaining multi-services. Experiment outcomes show the QOE-RAT selection method achieves much superior packet transmission outcomes when compared with state-of-art Rat selection methodologies.

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.

scholarly journals 5G/B5G Service Classification Using Supervised Learning

2021 ◽  
Vol 11 (11) ◽  
pp. 4942
Author(s):  
Jorge E. Preciado-Velasco ◽  
Joan D. Gonzalez-Franco ◽  
Caridad E. Anias-Calderon ◽  
Juan I. Nieto-Hipolito ◽  
Raul Rivera-Rodriguez
Keyword(s):  
Supervised Learning ◽  
Quality Of Experience ◽  
Service Providers ◽  
Matthews Correlation Coefficient ◽  
Service Level Agreement ◽  
Service Level ◽  
5G Networks ◽  
Distribution Of Resources

The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier.

scholarly journals Traffic Steering for eMBB in Multi-Connectivity Scenarios

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2063
Author(s):  
Jesús Burgueño ◽  
Isabel de-la-Bandera ◽  
David Palacios ◽  
Raquel Barco
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
5G Networks ◽  
Network Nodes ◽  
Opinion Score ◽  
Data Rates ◽  
Throughput Improvement ◽  
Radio Resources ◽  
Traffic Steering

Multi-connectivity (MC) is one of the most important features to be introduced in 5G networks, allowing User Equipment (UE) to simultaneously aggregate radio resources from several network nodes to enhance both data rates and reliability. Thus, this feature enables a further flexibility in the allocation of resources to the UEs in order to fulfil the users’ requirements in more complex 5G scenarios. This paper takes advantage of this wide flexibility to present a traffic steering approach that determines the amount of traffic to be held by each of the serving nodes in a multi-connectivity scenario. In this sense, the proposed technique is based on network and UE performance metrics in order to maximize the users’ perceived quality of experience (QoE) for enhanced Mobile Broadband (eMBB) services. It is then compared with a homogeneous traffic split among the serving nodes, with a single-connectivity approach and with state-of-the-art solutions. The benefits are analysed in terms of throughput and Mean Opinion Score (MOS), which is the main QoE metric. The analysis shows that a noticeable UE throughput improvement is reached when the proposed traffic steering method is applied. Consequently, this enhancement is noticed in the users’ QoE, which can lead to a reduction of operating expenses (OPEX) of the network.

scholarly journals A Window-Based, Server-Assisted P2P Network for VoD Services with QoE Guarantees

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Noé Torres-Cruz ◽  
Mario E. Rivero-Angeles ◽  
Gerardo Rubino ◽  
Ricardo Menchaca-Mendez ◽  
Rolando Menchaca-Mendez
Keyword(s):  
Mobile Networks ◽  
Quality Of Experience ◽  
Fluid Model ◽  
Video On Demand ◽  
P2p Network ◽  
Video Content ◽  
5G Networks ◽  
Video File ◽  
Definition Of

We describe a Peer-to-Peer (P2P) network that is designed to support Video on Demand (VoD) services. This network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also developed a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters.

scholarly journals Quality of Experience in 6G Networks: Outlook and Challenges

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Abd-Elhamid M. Taha
Keyword(s):  
Cellular Networks ◽  
Quality Of Experience ◽  
Low Latency ◽  
User Experiences ◽  
And Topology ◽  
Sixth Generation ◽  
High Bandwidth ◽  
Critical Characteristics

In this paper, we discuss the critical characteristics of user experience in sixth generation (6G) cellular networks. We first describe cellular networks’ evolution through 5G and then discuss the enabling technologies and projected services in 6G networks. We note that these networks are markedly centered around expanded intelligence, end-to-end resource and topology synchronization, and the intrinsic support to low-latency, high-bandwidth communication. These capabilities make context-rich, cyberphysical user experiences viable. It thereby becomes necessary to define and identify the role of quality of experience in 6G networks, especially when it comes to network management. We elaborate on these expected challenges and allude to viable opportunities in emerging technologies.

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