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Performance of Millimeter Wave Massive MIMO with the Alamouti Code

2018· article· en· W2883168090 on OpenAlexaff
Mohamed Alouzi, François Chan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMIMOComputer scienceElectronic engineering3G MIMOExtremely high frequencyBeamformingPath lossWirelessBandwidth (computing)Spectral efficiencyMulti-user MIMOTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Massive MIMO was recently proposed to achieve the advantage of conventional MIMO but on a greater scale. Massive MIMO can provide a much higher capacity without requiring more wireless spectrum. The need for higher data rate led researchers to consider the Millimeter Wave (mmW) band that offers a much larger unused bandwidth. Because of the higher path loss at mmW frequencies, and the poor scattering nature of the mmW channel (fewer paths exist), a hybrid beamforming technique with large antenna arrays and the Alamouti coding scheme are used in this paper to improve the performance of the single-cell mmW massive MIMO systems. Computer simulations have shown that a gain of 20 dB or more can be achieved using the Alamouti code.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score0.188

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 head0.009
GPT teacher head0.196
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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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