Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network.

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2019. 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.

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure.Sensors (Basel, Switzerland) · 2022
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

3 authors.

Han WangCollege of Physical Science & Engineering, Yichun University, Yichun 336000, China. hanwang1214@126.com.ORCID 0000-0001-7347-3763

Lingwei XuDepartment of Information Science & Technology, Qingdao University of Science & Technology, Qingdao 266061, China. bh054@qust.edu.cn.ORCID 0000-0002-2169-6356

Xianpeng WangState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Hakou 570228, China. wxpeng2016@hainu.edu.cn.

Funding

National Natural Science Foundation of China 61901409, 61961013, 61701144

6 · The paper itself

Abstract

This paper investigates outage probability (OP) performance predictions using transmit antenna selection (TAS) and derives exact closed-form OP expressions for a TAS scheme. It uses Monte-Carlo simulations to evaluate OP performance and verify the analysis. A back-propagation (BP) neural network-based OP performance prediction algorithm is proposed and compared with extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and BP neural network methods. The proposed method was found to have higher OP performance prediction results than the other prediction methods.

Indexed as

BP neural networkmobile cooperative communicationoutage probabilityperformance prediction

Identifiers

PMID31689926

DOI10.3390/s19214789

PMCPMC6865082

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

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.