Enhancing Quality of Experience in Telecommunication Networks: A Review of Frameworks and Machine Learning Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911853369622528 |
|---|---|
| author | Panahi, Parsa H. S. Jalilvand, Amir H. Diyanat, Abolfazl |
| author_facet | Panahi, Parsa H. S. Jalilvand, Amir H. Diyanat, Abolfazl |
| contents | The Internet service provider industry is currently experiencing intense competition as companies strive to provide top-notch services to their customers. Providers are introducing cutting-edge technologies to enhance service quality, understanding that their survival depends on the level of service they offer. However, evaluating service quality is a complex task. A crucial aspect of this evaluation lies in understanding user experience, which significantly impacts the success and reputation of a service or product. Ensuring a seamless and positive user experience is essential for attracting and retaining customers. To date, much effort has been devoted to developing tools for measuring Quality of Experience (QoE), which incorporate both subjective and objective criteria. These tools, available in closed and open-source formats, are accessible to organizations and contribute to improving user experience quality. This review article delves into recent research and initiatives aimed at creating frameworks for assessing user QoE. It also explores the integration of machine learning algorithms to enhance these tools for future advancements. Additionally, the article examines current challenges and envisions future directions in the development of these measurement tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Quality of Experience in Telecommunication Networks: A Review of Frameworks and Machine Learning Algorithms Panahi, Parsa H. S. Jalilvand, Amir H. Diyanat, Abolfazl Networking and Internet Architecture The Internet service provider industry is currently experiencing intense competition as companies strive to provide top-notch services to their customers. Providers are introducing cutting-edge technologies to enhance service quality, understanding that their survival depends on the level of service they offer. However, evaluating service quality is a complex task. A crucial aspect of this evaluation lies in understanding user experience, which significantly impacts the success and reputation of a service or product. Ensuring a seamless and positive user experience is essential for attracting and retaining customers. To date, much effort has been devoted to developing tools for measuring Quality of Experience (QoE), which incorporate both subjective and objective criteria. These tools, available in closed and open-source formats, are accessible to organizations and contribute to improving user experience quality. This review article delves into recent research and initiatives aimed at creating frameworks for assessing user QoE. It also explores the integration of machine learning algorithms to enhance these tools for future advancements. Additionally, the article examines current challenges and envisions future directions in the development of these measurement tools. |
| title | Enhancing Quality of Experience in Telecommunication Networks: A Review of Frameworks and Machine Learning Algorithms |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2404.16787 |