Matches in SemOpenAlex for { <https://semopenalex.org/work/W2808832022> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W2808832022 abstract "We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog two-stage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters. Thereby the number of angles of arrival (AoAs) and angles of departure (AoDs) with significant signal components is limited, and compressive sensing techniques can be leveraged for estimating the channel. In this paper, we investigate two sparse recovery algorithms: a Bayesian and non-Bayesian one. In the Bayesian approach, we invoke the sparse Bayesian learning (SBL) framework, which relies on a 2-layer hierarchical prior model for channel. A highly efficient and fast iterative Bayesian inference method is then applied to the proposed model. The non-Bayesian approach is a LASSO-based approach, where we devise a low complexity solution by adopting alternating directions method of multipliers (ADMM) technique to solve the problem. The efficacy of the proposed algorithms is demonstrated using numerical examples. The Bayesian approach shows improved estimation performance in relation to the non-Bayesian approach." @default.
- W2808832022 created "2018-06-29" @default.
- W2808832022 creator A5028268404 @default.
- W2808832022 creator A5029534229 @default.
- W2808832022 creator A5055356541 @default.
- W2808832022 date "2018-06-01" @default.
- W2808832022 modified "2023-09-26" @default.
- W2808832022 title "Bayesian Learning Based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Array" @default.
- W2808832022 cites W1633751774 @default.
- W2808832022 cites W1648445109 @default.
- W2808832022 cites W2054692642 @default.
- W2808832022 cites W2075148031 @default.
- W2808832022 cites W2107861471 @default.
- W2808832022 cites W2109449402 @default.
- W2808832022 cites W2111953900 @default.
- W2808832022 cites W2126607811 @default.
- W2808832022 cites W2148154358 @default.
- W2808832022 cites W2164278908 @default.
- W2808832022 cites W2208602793 @default.
- W2808832022 cites W2339667469 @default.
- W2808832022 cites W2402526535 @default.
- W2808832022 doi "https://doi.org/10.1109/spawc.2018.8445972" @default.
- W2808832022 hasPublicationYear "2018" @default.
- W2808832022 type Work @default.
- W2808832022 sameAs 2808832022 @default.
- W2808832022 citedByCount "3" @default.
- W2808832022 countsByYear W28088320222019 @default.
- W2808832022 countsByYear W28088320222021 @default.
- W2808832022 crossrefType "proceedings-article" @default.
- W2808832022 hasAuthorship W2808832022A5028268404 @default.
- W2808832022 hasAuthorship W2808832022A5029534229 @default.
- W2808832022 hasAuthorship W2808832022A5055356541 @default.
- W2808832022 hasBestOaLocation W28088320222 @default.
- W2808832022 hasConcept C107673813 @default.
- W2808832022 hasConcept C11413529 @default.
- W2808832022 hasConcept C127162648 @default.
- W2808832022 hasConcept C154945302 @default.
- W2808832022 hasConcept C160234255 @default.
- W2808832022 hasConcept C21822782 @default.
- W2808832022 hasConcept C41008148 @default.
- W2808832022 hasConcept C54197355 @default.
- W2808832022 hasConcept C62191587 @default.
- W2808832022 hasConcept C76155785 @default.
- W2808832022 hasConceptScore W2808832022C107673813 @default.
- W2808832022 hasConceptScore W2808832022C11413529 @default.
- W2808832022 hasConceptScore W2808832022C127162648 @default.
- W2808832022 hasConceptScore W2808832022C154945302 @default.
- W2808832022 hasConceptScore W2808832022C160234255 @default.
- W2808832022 hasConceptScore W2808832022C21822782 @default.
- W2808832022 hasConceptScore W2808832022C41008148 @default.
- W2808832022 hasConceptScore W2808832022C54197355 @default.
- W2808832022 hasConceptScore W2808832022C62191587 @default.
- W2808832022 hasConceptScore W2808832022C76155785 @default.
- W2808832022 hasLocation W28088320221 @default.
- W2808832022 hasLocation W28088320222 @default.
- W2808832022 hasOpenAccess W2808832022 @default.
- W2808832022 hasPrimaryLocation W28088320221 @default.
- W2808832022 hasRelatedWork W2402526535 @default.
- W2808832022 hasRelatedWork W2741899213 @default.
- W2808832022 hasRelatedWork W2810297269 @default.
- W2808832022 hasRelatedWork W2848225903 @default.
- W2808832022 hasRelatedWork W2910407148 @default.
- W2808832022 hasRelatedWork W2914020402 @default.
- W2808832022 hasRelatedWork W2919343695 @default.
- W2808832022 hasRelatedWork W2919441940 @default.
- W2808832022 hasRelatedWork W2950738210 @default.
- W2808832022 hasRelatedWork W2952763425 @default.
- W2808832022 hasRelatedWork W2963669302 @default.
- W2808832022 hasRelatedWork W2990352483 @default.
- W2808832022 hasRelatedWork W2996528919 @default.
- W2808832022 hasRelatedWork W3032095193 @default.
- W2808832022 hasRelatedWork W3033179367 @default.
- W2808832022 hasRelatedWork W3081486000 @default.
- W2808832022 hasRelatedWork W3107959189 @default.
- W2808832022 hasRelatedWork W3180655257 @default.
- W2808832022 hasRelatedWork W3186916931 @default.
- W2808832022 hasRelatedWork W3002549410 @default.
- W2808832022 isParatext "false" @default.
- W2808832022 isRetracted "false" @default.
- W2808832022 magId "2808832022" @default.
- W2808832022 workType "article" @default.