Publication
Wireless Channel Prediction Using Artificial Intelligence with Constrained Data Sets
| dc.contributor.author | Javanmardi, Gowhar | |
| dc.contributor.author | Samano-Robles, Ramiro | |
| dc.date.accessioned | 2023-01-23T12:44:26Z | |
| dc.date.embargo | 2035 | |
| dc.date.issued | 2022-09-12 | |
| dc.description.abstract | This work deals with the study of artificial intelligence (AI) tools for purposes of vehicular wireless channel prediction. The objective is to test the ability of different types of AI and machine learning (ML) algorithms under different types of implementation constraints. We focus particularly in highly changing scenarios where the channel state information changes relatively fast and therefore the relevant measurements or long-term statistical models are therefore scarce. This means that the training of our models can be potentially inaccurate or incomplete and we need to investigate which AI algorithm behaves better in this challenging condition. In future work we aim to investigate also computation complexity constraints, real-time deadlines, and outdated/distorted or noisy data set samples. We also aim to correlate the main properties of the well-known Jakes' channel model with the effectiveness of the type of prediction and the parameters of the different algorithms being tested. The objective of channel prediction in vehicular networks is to reduce allocation and transmission errors, thereby reducing latency and improving message transmission reliability, which is crucial for future applications such as autonomous vehicles. Results show that even in situations with incomplete data sets, AI can provide good approximate predictions on the channel outcome. | pt_PT |
| dc.description.sponsorship | This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Agreement, through the European Regional Development Fund (ERDF), and by FCT, under project POCI-01-0145-FEDER-032218 (5GSDN); also by FCT and the ESF (European Social Fund) through the Regional Operational Programme (ROP) Norte 2020. InSecTT project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 876038. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey. Disclaimer: The document reflects only the author’s view and the Commission is not responsible for any use that may be made of the information it contains. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.23919/MIKON54314.2022.9925006 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/21773 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation | UIDP/UIDB/04234/2020 | pt_PT |
| dc.relation | POCI-01-0145-FEDER-032218 (5GSDN) | pt_PT |
| dc.relation | Intelligent Secure Trustable Things | |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9925006 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Channel prediction | pt_PT |
| dc.subject | Artificial intelligence | pt_PT |
| dc.subject | Jake’s mode | pt_PT |
| dc.title | Wireless Channel Prediction Using Artificial Intelligence with Constrained Data Sets | pt_PT |
| dc.title.alternative | 220609 | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Intelligent Secure Trustable Things | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/876038/EU | |
| oaire.citation.title | 24th International Microwave and Radar Conference (MIKON) | pt_PT |
| oaire.fundingStream | H2020 | |
| person.familyName | Samano-Robles | |
| person.givenName | Ramiro | |
| person.identifier.ciencia-id | 0717-6659-467C | |
| person.identifier.orcid | 0000-0002-1054-1818 | |
| person.identifier.scopus-author-id | 23477889200 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.name | European Commission | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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