Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, (KOR).ORCID iD: 0000-0002-3743-0309
University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering. (KAMPT)ORCID iD: 0000-0002-9756-1909
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, (KOR).ORCID iD: 0000-0002-1534-061X
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, (KOR).ORCID iD: 0000-0002-2240-1337
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 141277-141288Article in journal (Refereed) Published
Abstract [en]

Tactile Internet (TI) has very stringent networking requirements and the transport layer plays a crucial role in meeting these requirements. However, the transport layer has several inherent limitations (e.g., bufferbloat, incast issue, and head of line blocking) due to which the performance of the current transport layer solutions is not optimal. We advocate replacing the “store-and-forward” strategy in transport layer solutions with the “compute-and-forward” strategy. One way to implement the “compute-and-forward” strategy is random linear network coding (RLNC). This paper proposes a learning-based RLNC framework called RS-RLNC that utilizes network and receiver feedback to optimally select between block-RLNC and sliding-RLNC to improve overall network performance. We present a simulation-based performance evaluation of current transport layer solutions against the state-of-the-art RLNC and RS-RLNC in terms of throughput, latency, and decoding complexity. Delay is reduced by a factor of 8.5% and decoding complexity is improved up to 20% compared to the state-of-the-art. Simulation results indicate that RS-RLNC has the potential to meet the stringent requirements of TI applications. Additionally, we present three future directions outlining the evolution of RS-RLNC to enhance the transport layer for TI compatibility.

Place, publisher, year, edition, pages
IEEE, 2023. Vol. 11, p. 141277-141288
Keywords [en]
Network coding, RLNC, reinforcement learning, tactile internet, URLLC
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hv:diva-21119DOI: 10.1109/access.2023.3340210ISI: 001126241900001Scopus ID: 2-s2.0-85179795454OAI: oai:DiVA.org:hv-21119DiVA, id: diva2:1821919
Note

CC-BY 4.0

Available from: 2023-12-21 Created: 2023-12-21 Last updated: 2024-01-08Bibliographically approved

Open Access in DiVA

fulltext(1868 kB)62 downloads
File information
File name FULLTEXT01.pdfFile size 1868 kBChecksum SHA-512
6f1ff125c8a301e25e656e2b360e23e456195884152a9e855bee5b478891c6a4bc64fb850861e1fd2cc76798a3070f9478e1e32eba8c8afad37bb1b3036fca5d
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Shahzad, .Ali, RashidHaider, AmirKim, Hyung Seok
By organisation
Division of Mathematics, Computer and Surveying Engineering
In the same journal
IEEE Access
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 62 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 59 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf