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 Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Diego José Luis Botia Valderrama ◽  
Natalia Gaviria Gómez
Keyword(s):  
Neural Networks ◽  
Quality Of Experience ◽  
Video Quality ◽  
Similarity Index ◽  
Correlation Coefficients ◽  
Structural Similarity ◽  
Opinion Score ◽  
Measurement And Evaluation ◽  
Rate Correlation

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

scholarly journals Role of 5G Networks: Issues, Challenges and Applications

2019 ◽  
Vol 8 (6) ◽  
pp. 3172-3178
Keyword(s):  
Daily Life ◽  
Handheld Devices ◽  
Network Technology ◽  
Data Traffic ◽  
5G Networks ◽  
Data Rates ◽  
5G Network ◽  
Day By Day

Due to revolutionary development in electronic and communication, mobile and handheld devices become the part of our daily life. As a result, volume of data traffic on Internet is increasing day by day. To provide unlimited, uninterrupted and content-rich services to these devices, the 5th Generation (5G) of network technology is emerged. 5G network can provide better Quality of Service (QoS) along with higher data rates than 4G network and have least latency. The paper appraisals various generations of wireless networks. Furthermore, it explores various challenges in implementation of 5G network and application areas of 5G network

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 Analysis of the Cost of Varying Levels of User Perceived Quality for Internet Access

2020 ◽  
Author(s):  
Ali Adib Arnab ◽  
John Schormans ◽  
Sheikh Razibulhasan Raj ◽  
Nafi Ahmad
Keyword(s):  
Quality Of Service ◽  
User Experience ◽  
Quality Of Experience ◽  
Internet Access ◽  
End User ◽  
Opinion Score ◽  
Commercial Organizations ◽  
The Cost ◽  
Qos Metrics

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

A quantitative relationship between Application Performance Metrics and Quality of Experience for Over-The-Top video

2018 ◽  
Vol 142 ◽  
pp. 194-207 ◽  
Author(s):  
Weiwei Li ◽  
Petros Spachos ◽  
Mark Chignell ◽  
Alberto Leon-Garcia ◽  
Leon Zucherman ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
Quantitative Relationship ◽  
Application Performance ◽  
Over The Top

scholarly journals Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Muhammad Saleem ◽  
Yasir Saleem ◽  
H. M. Shahzad Asif ◽  
M. Saleem Mian
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
Optimal Solution ◽  
Content Delivery ◽  
Multimedia Streaming ◽  
Multimedia Content ◽  
Quality Level ◽  
Limited Bandwidth ◽  
Multimedia Content Delivery

The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over HTTP is an efficient scheme for bitrate adaptation in which video is segmented and stored in different quality levels. The multimedia streaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We have proposed an adaptive rate control using enhanced Double Deep Q-Learning approach to improve multimedia content delivery by switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly evaluated against state-of-the-art heuristic and learning-based algorithms. The performance metrics such as PSNR, SSIM, quality of experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which shows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm provides faster convergence to the optimal solution as compared to other algorithms considered in our work.

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 An Optimized User Prioritized Service Provisioning in LTE Network

2020 ◽  
Vol 8 (6) ◽  
pp. 5093-5297
Keyword(s):  
Quality Of Experience ◽  
Scheduling Algorithm ◽  
System Capacity ◽  
Network Simulator ◽  
Service Provisioning ◽  
Lte Network ◽  
Exponential Increase ◽  
Radio Resources ◽  
User Priority

An exponential increase in the number of multimedia users over LTE network necessitates user prioritization and differentiated services support. LTE standard has defined Quality of Service (QoS) class-based user priority rather than priority among users within a QoS class. However, when the cell load exceeds the system capacity, Quality of Experience (QoE) of all the users may deteriorate due to lack of radio resources allocated to them. Under these circumstances, some users of interest may be prioritized over other users in the cell during resource allocation to enhance their QoE. In this paper, Proportional Fair (PF) scheduling algorithm Based User Priority (PFBUP) mechanism is proposed to prioritize organizational users over other users. Performance evaluation of the proposed mechanism is carried out using QualNet 7.1 network simulator by varying priority coefficient for the organizational users.

scholarly journals Enhancing Quality of Experience in Malay Language Dyslexia Screening Test

2021 ◽  
Vol 15 ◽  
pp. 99-103
Author(s):  
Muhammad Hanif Jofri ◽  
Mohd Norasri Ismail ◽  
Mohd Farhan Md Fudzee ◽  
Muhammad Hatta Mohamed Ali @ Md Hani
Keyword(s):  
Special Education Teachers ◽  
School System ◽  
Screening Test ◽  
Quality Of Experience ◽  
Screening Tests ◽  
Usual Practice ◽  
Mean Values ◽  
Opinion Score ◽  
The Mean

Identifying dyslexia among Malaysian citizens, especially children nowadays is a prominent issue. The usual practice of dyslexia screening tests in the Malaysian school system is by teacher's observation and intervention. However, this is usually time-consuming, less accurate and lacking additional supporting tools. Moreover, dyslexic children enrolling in a normal education system will encounter many problems for the teacher and the children themselves. A Malay language mobile-based application for a dyslexia screening test named Kiddo Disleksia has been developed to solve this issue. However, it has not been tested in terms of Quality of Experience (QoE) as well as usability. Therefore, this research aims to test the QoE level of Kiddo Disleksia and also to compare the traditional dyslexia screening test with Kiddo Disleksia in terms of usability. To test the QoE, several special education teachers are required to rate Kiddo Disleksia using Mean Opinion Score (MOS). Ten children were tested using Kiddo Disleksia and 80% of them recognized as dyslexic. This result also similar with the traditional paper-based screening test. Therefore, the Kiddo Disleksia application is considered reliable for dyslexia screening tests for children. For QoE, the results show that the mean values of MOS are 3.9 and above. Therefore, the quality of experience during dyslexia screening tests can be enhanced using Kiddo Disleksia.

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