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Record W2979986472 · doi:10.1109/ccece.2019.8861976

Joint Iterative Training-based Hybrid Precoding and Combining for Millimeter Wave Systems

2019· article· en· W2979986472 on OpenAlexaff
Ali Mohebbi, Wei‐Ping Zhu, M. Omair Ahmad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsConcordia University
Fundersnot available
KeywordsPrecodingComputer scienceExtremely high frequencySingular value decompositionMIMOSpectral efficiencyIterative methodComputationKey (lock)Computational complexity theoryJoint (building)AlgorithmChannel (broadcasting)Electronic engineeringComputer engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Hybrid precoding and combining is a key solution to reduce the hardware cost and power consumption of millimeter wave MIMO systems. However, estimating the entire channel matrix required for designing such a solution is very challenging. In this paper, we propose a joint iterative training based algorithm to design the digital precoder and combiner in the hybrid structure. We show that the proposed method can eliminate channel estimation and SVD computation needed for obtaining the precoders and combiners. Simulation results show that the proposed approach performs comparable to existing SVD-based methods in terms of spectral efficiency but with much less computational complexity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

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

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 head0.058
GPT teacher head0.223
Teacher spread0.164 · 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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

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