Evidence map›Paper›PMID 40890208›Full record

ArticleScientific reports2025

Scheduling allocation in 5G slicing networks utilizing weighted exponential and logarithmic functions to improve QoS.

Xiaofeng Nong, Xiaobo Liang

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Xiaofeng NongNetwork and Information Center, Guilin Tourism University, Guilin, 541000, Guangxi, China.
Xiaobo LiangNetwork and Educational Technology Center, Guilin Normal College, Guilin, 541000, Guangxi, China. xiaobo_liang@gltu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Offering media-rich services, such as streaming videos, for emergency services requires compliance with reliability standards. The deployment of fifth-generation (5G) networks enables a wide range of services and applications with diverse Quality of Service (QoS) requirements. Supporting heterogeneous performance and migrating vital services to 5G networks pose significant challenges for emergency service providers in maintaining QoS. To address this, schedulers allocate resources to various traffic types in a QoS- and channel state-aware manner. The exponential function scheduling method (EXP RULE) is a well-established approach; however, it requires optimization to reduce packet loss rate and latency. This study proposes the Hybrid Weighted Exponential and Logarithmic Rule (HWEL RULE), which enhances EXP RULE by integrating weighted logarithmic functions to improve QoS metrics in 5G slicing networks. Operating within a Fog Radio Access Network (F-RAN) framework with Network Functions Virtualization (NFV), HWEL RULE dynamically allocates Baseband Unit (BBU) resources to Ultra-Reliable Low-Latency Communications (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). Using LTE-Sim simulations, HWEL RULE demonstrates up to a 30.06% reduction in packet loss rate, 21% lower latency for video traffic, 23.5% lower latency for VoIP, 8.6% higher throughput, and 1.2% improved fairness compared to EXP RULE. This incremental enhancement ensures compatibility with existing 5G architectures while significantly improving real-time traffic performance.

Indexed as

5G scheduling algorithm5G slicing networksLive broadcast trafficQuality of serviceRadio resource allocationWeighted combination of exponential-logarithmic rules (HWEL)

Identifiers

PMID40890208
PMCPMC12402267

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.