Treffer: Research on data transmission system based on expert library reinforcement learning in integrated network.

Title:
Research on data transmission system based on expert library reinforcement learning in integrated network.
Authors:
Xing Z; Shanghai Donghai College, Shanghai, China.
Source:
PloS one [PLoS One] 2025 Nov 25; Vol. 20 (11), pp. e0333372. Date of Electronic Publication: 2025 Nov 25 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science
References:
Biomed Microdevices. 2015 Dec;17(6):115. (PMID: 26564448)
IEEE J Biomed Health Inform. 2024 Jun;28(6):3341-3348. (PMID: 37531307)
IEEE Trans Neural Netw Learn Syst. 2025 Mar;36(3):4779-4793. (PMID: 38551826)
IEEE Trans Neural Netw Learn Syst. 2025 Jun;36(6):9737-9757. (PMID: 40030358)
Entry Date(s):
Date Created: 20251125 Date Completed: 20251125 Latest Revision: 20251128
Update Code:
20251128
PubMed Central ID:
PMC12646453
DOI:
10.1371/journal.pone.0333372
PMID:
41289321
Database:
MEDLINE

Weitere Informationen

With the continuous advancement of network transmission technology, more and more applications are being applied in wireless network environments, especially in places that require high coverage, such as oceans and mountainous areas. However, wireless data transmission has the disadvantages of unstable transmission and easy interruption using traditional methods. Based on this, we propose a data transmission system that uses a micro-electron-mechanical system (MEMS) sensor to obtain the wireless network status and applies expert library reinforcement learning that does not rely on reward functions to achieve retrieval enhancement of data transmission. Experimental verification shows that the proposed expert library reinforcement learning has strong generalizability and fast convergence. Expert library reinforcement learning, wireless network, MEMS, integrated network.
(Copyright: © 2025 Xing. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The authors have declared that no competing interests exist.