Evidence map›Paper›PMID 39966615›Full record

ArticleScientific reports2025

Compressive transmission scheme for power regulation of embedded 5G communication devices.

Min Zhu, Xianghua Liu, Zhanxiang Ye, Salwa Othmen, Ayman Alfahid, Aymen Flah, Zdenek Slanina

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Min ZhuCollage of Information Science andTechnology, Zhejiang Shuren University, Hangzhou, 310015, China. zhumin@zjsru.edu.cn.
Xianghua LiuSchool of Artificial Intelligence, Wenzhou Polytechnic College, Wenzhou, 325000, People's Republic of China.
Zhanxiang YeSchool of Artificial Intelligence, Wenzhou Polytechnic College, Wenzhou, 325000, People's Republic of China.
Salwa OthmenDepartment of Computers and Information Technologies, College of Sciences and Arts Turaif, Northern Border University, Arar, 91431, Saudi Arabia. salwa.othmen@nbu.edu.sa.
Ayman AlfahidDepartment of Information Systems, College of Computer and Information Sciences, Majmaah University, Al Majma'ah, 11952, Saudi Arabia. e.alfahed@mu.edu.sa.
Aymen FlahCollege of Engineering, University of Business and Technology (UBT), 21448, Jeddah, Saudi Arabia.
Zdenek SlaninaENET Centre, VSB-Technical University of Ostrava, Ostrava, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Power management for embedded devices in Fifth Generation (5G) networks is mandatory for synchronizing the communication between the devices. In such cases, the need for integration power optimization is recommended aiding lossless and high-speed communications. To suppress the issues in embedded hardware-based power failures during transmissions, this article proposes a Compressive Transmission Scheme (CTS) through Power Regulation (PR). The proposed scheme identifies multiple transmission possibilities under low power and high throughput constraints of 5G in a single interval. The device integrations are decided by the available devices under power-efficient transmission slots. Such allocation slots are defined for integrated transmission using neural-diffracted networks. The learning network defines the objectives for transmission between embedded hardware and the 5G device under low power. This is pursued until the transmission is completed; the adverse energy drain impact is handled by offloading the slots to the active hardware available. This balances the power management to prevent communication loss satisfying the 5G constraints. For the maximum slots/device, the proposed scheme achieves 11.46% high slot allocation, 12.47% low latency, and 9.99% less power consumption.

Indexed as

5GCompressed transmissionEmbedded devicesNeural networkPower management

Identifiers

PMID39966615
PMCPMC11836382

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.