scholarly journals A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 318
Author(s):  
Merima Kulin ◽  
Tarik Kazaz ◽  
Eli De Poorter ◽  
Ingrid Moerman
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Performance Improvement ◽  
Quality Of Experience ◽  
Data Driven ◽  
Protocol Stack ◽  
Comprehensive Survey ◽  
Network Prediction ◽  
Network Quality

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

Quality of Experience Models for Multimedia Streaming

2010 ◽  
Vol 2 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Vlado Menkovski ◽  
Georgios Exarchakos ◽  
Antonio Liotta ◽  
Antonio Cuadra Sánchez
Keyword(s):  
Machine Learning ◽  
Quality Of Experience ◽  
Prediction Models ◽  
Multimedia Streaming ◽  
Accurate Estimation ◽  
Management Service ◽  
Subjective Data ◽  
Efficient Management ◽  
Learning Techniques

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience (QoE) is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

Opportunities for improvement (OFI) in guideline concordance (GLC) at National Comprehensive Cancer Network (NCCN) member institutions (MI).

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 172-172
Author(s):  
Jennifer Lyle ◽  
Jonathan L. Vandergrift ◽  
Kimary Kulig ◽  
Keyword(s):  
Quality Of Care ◽  
Performance Improvement ◽  
Institutional Delivery ◽  
Data Driven ◽  
Provider Education ◽  
Post Intervention ◽  
Improvement Programs ◽  
Quality Improvement Programs ◽  
Cancer Network

172 Background: The NCCN is implementing a performance improvement initiative using breast cancer (BC) practice data from NCCN MIs to improve institutional delivery of GLC and identify tailored opportunities for improving efficiency and quality of care delivered. Methods: This initiative includes evaluation of baseline GLC, review of non-concordant (NC) cases, design of tailored institutional interventions, and post-intervention evaluation of GLC and reasons for NC. BC patients presenting from July 2007 to March 2009 at 11 NCCN MIs were included in the baseline review. Six Category 1 GLC and 3 ASCO/NCCN quality measures evaluating adjuvant chemotherapy (CTX), endocrine therapy (ET), and radiation (XRT) were reviewed. GLC was assessed using the NCCN Outcomes database. Results: Aggregate GLC across all measures was 90% (MI range 66% to 100%). Review of NC cases was used to develop tailored OFI interventions. Three MIs are focusing on clinical practice improvement via provider education and feedback and integration of electronic medical record flags for treatment consideration. Nine MIs are working on improving access and reducing time-to-treatment lags. Currently, half of MIs are implementing and half are evaluating OFI interventions. Conclusions: This program supports data-driven QI efforts at MIs with the goal of improving efficiency and quality of care delivered to patients at participating sites, as well as serving as a model for data-driven quality improvement programs. [Table: see text]

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