Predictive Estimation of Wireless Link Performance from Medium Physical Parameters
type: Dataset
description: In wireless networks, the physical medium is the cause of most of the errors and performance drops. Thus, an efficient predictive estimation of wireless networks performance w.r.t. medium status by the communication peers would be a leap ahead in the improvement of wireless communication. For that purpose, we designed a measurement bench that allows us to accurately control the noise level on an unidirectional WIFI communication link in the protected environment of an anechoic room. This way, we generated different medium conditions and collected several measurements for various PHY layer parameters on that link. Using the collected data we analyzed the ability to predictively estimate the throughput performance of a noisy wireless link from measured physical medium parameters, using SVR (Support Vector Regression). Finally, we ranked the pertinence of the most common physical parameters for estimating or predicting the throughput that can be expected by users on top of the IP layer over a WIFI link.
issued: 2026-05-09T18:18:52.313563
language: en
modified: 2026-05-09T18:18:52.313567
contact point: Organization
  fn: CCSD
keyword: data-mining
keyword: datasets
keyword: infoeu-reposemanticsreport
keyword: infoinfo-nicomputer-science-csnetworking-and-internet-architecture-csni
keyword: machine-learning
keyword: measurement-bed
keyword: predictive-estimation
keyword: reports
keyword: wireless-networks
landing page: https://hal.science/hal-00861659

Predictive Estimation of Wireless Link Performance from Medium Physical Parameters
type: Distribution
format: HTML
issued: 2026-05-09T18:18:52.318363
modified: 2026-05-09T18:18:52.303547
access URL: https://hal.science/hal-00861659

test_moissonnage_selune
type: Agent