Matches in SemOpenAlex for { <https://semopenalex.org/work/W2913291195> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2913291195 endingPage "20415" @default.
- W2913291195 startingPage "20404" @default.
- W2913291195 abstract "This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming setup comprising an analog beamforming (ABF) network with adjustable beamwidth followed by a zero-forcing baseband processing block. The goal is to select the optimal configuration for the ABF network based on the estimated AoAs of the various user equipments. To that aim, we consider 1) two supervised machine-learning approaches: k -nearest neighbors (kNN) and support vector classifiers (SVC); and 2) a feed-forward deep neural network: the multilayer perceptron. We conduct an extensive performance evaluation to investigate the impact of the quality of CSI estimates (AoAs and powers) obtained via the Capon or MUSIC methods, fluctuations in the received power, the size of the training dataset, the total number of analog beamformers in the codebook, their beamwidth, or the number of active users. The computer simulations reveal that performance, in terms of classification accuracy and sum-rate is very close to that achievable via exhaustive search." @default.
- W2913291195 created "2019-02-21" @default.
- W2913291195 creator A5011088746 @default.
- W2913291195 creator A5085560572 @default.
- W2913291195 date "2019-01-01" @default.
- W2913291195 modified "2023-10-09" @default.
- W2913291195 title "Learning and Data-Driven Beam Selection for mmWave Communications: An Angle of Arrival-Based Approach" @default.
- W2913291195 cites W2103665886 @default.
- W2913291195 cites W2142308177 @default.
- W2913291195 cites W2477563955 @default.
- W2913291195 cites W2562947506 @default.
- W2913291195 cites W2734408173 @default.
- W2913291195 cites W2738272747 @default.
- W2913291195 cites W2789503661 @default.
- W2913291195 cites W2790223875 @default.
- W2913291195 cites W2793446253 @default.
- W2913291195 cites W2898434483 @default.
- W2913291195 cites W2963145597 @default.
- W2913291195 cites W2963190722 @default.
- W2913291195 cites W2963487795 @default.
- W2913291195 cites W653761051 @default.
- W2913291195 doi "https://doi.org/10.1109/access.2019.2895594" @default.
- W2913291195 hasPublicationYear "2019" @default.
- W2913291195 type Work @default.
- W2913291195 sameAs 2913291195 @default.
- W2913291195 citedByCount "50" @default.
- W2913291195 countsByYear W29132911952019 @default.
- W2913291195 countsByYear W29132911952020 @default.
- W2913291195 countsByYear W29132911952021 @default.
- W2913291195 countsByYear W29132911952022 @default.
- W2913291195 countsByYear W29132911952023 @default.
- W2913291195 crossrefType "journal-article" @default.
- W2913291195 hasAuthorship W2913291195A5011088746 @default.
- W2913291195 hasAuthorship W2913291195A5085560572 @default.
- W2913291195 hasBestOaLocation W29132911951 @default.
- W2913291195 hasConcept C119857082 @default.
- W2913291195 hasConcept C127759330 @default.
- W2913291195 hasConcept C138660444 @default.
- W2913291195 hasConcept C154945302 @default.
- W2913291195 hasConcept C179717631 @default.
- W2913291195 hasConcept C190060920 @default.
- W2913291195 hasConcept C21822782 @default.
- W2913291195 hasConcept C2776257435 @default.
- W2913291195 hasConcept C41008148 @default.
- W2913291195 hasConcept C50644808 @default.
- W2913291195 hasConcept C54197355 @default.
- W2913291195 hasConcept C65165936 @default.
- W2913291195 hasConcept C76155785 @default.
- W2913291195 hasConceptScore W2913291195C119857082 @default.
- W2913291195 hasConceptScore W2913291195C127759330 @default.
- W2913291195 hasConceptScore W2913291195C138660444 @default.
- W2913291195 hasConceptScore W2913291195C154945302 @default.
- W2913291195 hasConceptScore W2913291195C179717631 @default.
- W2913291195 hasConceptScore W2913291195C190060920 @default.
- W2913291195 hasConceptScore W2913291195C21822782 @default.
- W2913291195 hasConceptScore W2913291195C2776257435 @default.
- W2913291195 hasConceptScore W2913291195C41008148 @default.
- W2913291195 hasConceptScore W2913291195C50644808 @default.
- W2913291195 hasConceptScore W2913291195C54197355 @default.
- W2913291195 hasConceptScore W2913291195C65165936 @default.
- W2913291195 hasConceptScore W2913291195C76155785 @default.
- W2913291195 hasFunder F4320320300 @default.
- W2913291195 hasLocation W29132911951 @default.
- W2913291195 hasLocation W29132911952 @default.
- W2913291195 hasOpenAccess W2913291195 @default.
- W2913291195 hasPrimaryLocation W29132911951 @default.
- W2913291195 hasRelatedWork W2032825607 @default.
- W2913291195 hasRelatedWork W2040776150 @default.
- W2913291195 hasRelatedWork W2091540876 @default.
- W2913291195 hasRelatedWork W2776817167 @default.
- W2913291195 hasRelatedWork W2913291195 @default.
- W2913291195 hasRelatedWork W2919332200 @default.
- W2913291195 hasRelatedWork W4214917784 @default.
- W2913291195 hasRelatedWork W4323914567 @default.
- W2913291195 hasRelatedWork W4366728347 @default.
- W2913291195 hasRelatedWork W4385800701 @default.
- W2913291195 hasVolume "7" @default.
- W2913291195 isParatext "false" @default.
- W2913291195 isRetracted "false" @default.
- W2913291195 magId "2913291195" @default.
- W2913291195 workType "article" @default.