Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313525020> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W4313525020 abstract "Millimeter-wave communication systems design require accurate path-loss prediction, critical to determining coverage area and system capacity. In this work, four machine learning algorithms are proposed for path-loss prediction in an indoor environment for 5G millimeter-wave frequencies, from 26.5 to 40 GHz. They are artificial neural network, support vector regression, random forest, and gradient tree boosting. We compare their performances, including extensions of the empirical path-loss models alpha-beta-gamma and close-in frequency-dependent exponent incorporating the number of crossed walls. The results show that the ML techniques improve the prediction accuracy of empirical models." @default.
- W4313525020 created "2023-01-06" @default.
- W4313525020 creator A5019652782 @default.
- W4313525020 creator A5037372511 @default.
- W4313525020 creator A5053063454 @default.
- W4313525020 creator A5066814190 @default.
- W4313525020 date "2022-11-30" @default.
- W4313525020 modified "2023-10-17" @default.
- W4313525020 title "Path-Loss Prediction of Millimeter-wave using Machine Learning Techniques" @default.
- W4313525020 cites W1964357740 @default.
- W4313525020 cites W1993863804 @default.
- W4313525020 cites W2002016471 @default.
- W4313525020 cites W2116334496 @default.
- W4313525020 cites W2248726416 @default.
- W4313525020 cites W2487770199 @default.
- W4313525020 cites W2620784808 @default.
- W4313525020 cites W2808499605 @default.
- W4313525020 cites W2889980946 @default.
- W4313525020 cites W2911964244 @default.
- W4313525020 cites W2942443632 @default.
- W4313525020 cites W2944604179 @default.
- W4313525020 cites W2964124316 @default.
- W4313525020 cites W2980016373 @default.
- W4313525020 cites W2986489130 @default.
- W4313525020 cites W3009345526 @default.
- W4313525020 cites W3014079666 @default.
- W4313525020 cites W3129785922 @default.
- W4313525020 cites W3181358638 @default.
- W4313525020 cites W4213113494 @default.
- W4313525020 cites W429766147 @default.
- W4313525020 cites W2071189984 @default.
- W4313525020 doi "https://doi.org/10.1109/latincom56090.2022.10000523" @default.
- W4313525020 hasPublicationYear "2022" @default.
- W4313525020 type Work @default.
- W4313525020 citedByCount "1" @default.
- W4313525020 crossrefType "proceedings-article" @default.
- W4313525020 hasAuthorship W4313525020A5019652782 @default.
- W4313525020 hasAuthorship W4313525020A5037372511 @default.
- W4313525020 hasAuthorship W4313525020A5053063454 @default.
- W4313525020 hasAuthorship W4313525020A5066814190 @default.
- W4313525020 hasConcept C105795698 @default.
- W4313525020 hasConcept C11413529 @default.
- W4313525020 hasConcept C119857082 @default.
- W4313525020 hasConcept C12267149 @default.
- W4313525020 hasConcept C154945302 @default.
- W4313525020 hasConcept C169258074 @default.
- W4313525020 hasConcept C194273485 @default.
- W4313525020 hasConcept C2777735758 @default.
- W4313525020 hasConcept C31258907 @default.
- W4313525020 hasConcept C33923547 @default.
- W4313525020 hasConcept C41008148 @default.
- W4313525020 hasConcept C45764600 @default.
- W4313525020 hasConcept C46686674 @default.
- W4313525020 hasConcept C50644808 @default.
- W4313525020 hasConcept C555944384 @default.
- W4313525020 hasConcept C70153297 @default.
- W4313525020 hasConcept C76155785 @default.
- W4313525020 hasConcept C83546350 @default.
- W4313525020 hasConceptScore W4313525020C105795698 @default.
- W4313525020 hasConceptScore W4313525020C11413529 @default.
- W4313525020 hasConceptScore W4313525020C119857082 @default.
- W4313525020 hasConceptScore W4313525020C12267149 @default.
- W4313525020 hasConceptScore W4313525020C154945302 @default.
- W4313525020 hasConceptScore W4313525020C169258074 @default.
- W4313525020 hasConceptScore W4313525020C194273485 @default.
- W4313525020 hasConceptScore W4313525020C2777735758 @default.
- W4313525020 hasConceptScore W4313525020C31258907 @default.
- W4313525020 hasConceptScore W4313525020C33923547 @default.
- W4313525020 hasConceptScore W4313525020C41008148 @default.
- W4313525020 hasConceptScore W4313525020C45764600 @default.
- W4313525020 hasConceptScore W4313525020C46686674 @default.
- W4313525020 hasConceptScore W4313525020C50644808 @default.
- W4313525020 hasConceptScore W4313525020C555944384 @default.
- W4313525020 hasConceptScore W4313525020C70153297 @default.
- W4313525020 hasConceptScore W4313525020C76155785 @default.
- W4313525020 hasConceptScore W4313525020C83546350 @default.
- W4313525020 hasLocation W43135250201 @default.
- W4313525020 hasOpenAccess W4313525020 @default.
- W4313525020 hasPrimaryLocation W43135250201 @default.
- W4313525020 hasRelatedWork W2767034401 @default.
- W4313525020 hasRelatedWork W3166976141 @default.
- W4313525020 hasRelatedWork W3195168932 @default.
- W4313525020 hasRelatedWork W3200719183 @default.
- W4313525020 hasRelatedWork W4288057626 @default.
- W4313525020 hasRelatedWork W4292373754 @default.
- W4313525020 hasRelatedWork W4293069612 @default.
- W4313525020 hasRelatedWork W4304142064 @default.
- W4313525020 hasRelatedWork W4308654587 @default.
- W4313525020 hasRelatedWork W4328133444 @default.
- W4313525020 isParatext "false" @default.
- W4313525020 isRetracted "false" @default.
- W4313525020 workType "article" @default.