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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption. |
| Authors Name: | Shravani Amar , M.Mounika |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_189092
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| Published Paper Id: | IJNRD2303280 |
| Published In: | Volume 8 Issue 3, March-2023 |
| DOI: | |
| Abstract: | The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite important for network optimization. The current 5G and conceived 6G network in the future with ultra high density, bandwidth, mobility and large scale brings urgent requirement of high efficient end-to-end optimization methods. The conventional network optimization methods without learning and intelligent decision ability are hard to handle the high complexity and dynamic scenarios of 6G. Recently, machine learning based QoS and QoE aware network optimization algorithms emerge as a hot research area and attract much attention, which is widely acknowledged as the poten- tial solution for end-to-end optimization in 6G. However, there are still many critical issues of employing machine learning in networks, especially in 6G. In this paper, we give a compre- hensive survey on the recent machine learning based network optimization methods to guarantee the end-to-end QoS and QoE. To easy to follow, we introduce the investigated works following the end-to-end transmission flow from network access, routing to network congestion control and adaptive steaming control. Then we discuss some open issues and potential future research directions. |
| Keywords: | End-to-end, quality of service (QoS), quality of experience (QoE), machine learning (ML), deep learning (DL), network access, resource allocation, channel assignment, routing, congestion control, adaptive streaming control, adaptive bitrate streaming (ABR). |
| Cite Article: | "Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption.", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 3, page no.c712-c728, March-2023, Available :http://www.ijnrd.org/papers/IJNRD2303280.pdf |
| Downloads: | 000118749 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2303280 Registration ID: 189092 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: c712-c728 Country: hyderabad, Telangana, Inida Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2303280 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2303280 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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