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Record W4226177089 · doi:10.1109/lcomm.2022.3163668

3-D Antenna Array Structures for Millimeter Wave Multi-User Massive MIMO Hybrid Precoder Design: A Performance Comparison

2022· article· en· W4226177089 on OpenAlexafffund
Mobeen Mahmood, Asil Koç, Tho Le‐Ngoc

Bibliographic record

VenueIEEE Communications Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaHuawei Technologies
KeywordsBasebandPrecodingMIMOComputer scienceSingular value decompositionBeamformingExtremely high frequencyElectronic engineeringSpectral efficiencyAntenna (radio)Channel (broadcasting)Antenna arrayMulti-user MIMOInterference (communication)Topology (electrical circuits)AlgorithmTelecommunicationsBandwidth (computing)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this letter, we investigate the performance of various three-dimensional (3D) array structures in the design of hybrid precoder (HP) for millimeter wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems. The proposed HP scheme involves two stages: (i) RF beamforming based on singular value decomposition (SVD) of channel matrix, and baseband MU precoding based on the instantaneous effective baseband channel to mitigate MU-interference by a regularized zero-forcing (RZF) technique. Illustrative results show that, in comparison to two-dimensional (2D) arrays, 3D array structures can provide high spectral and energy efficiencies as well as non-varying achievable rate independent from the user angular location.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.277
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2022
Admission routes2
Has abstractyes

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