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Record W4285283743 · doi:10.1109/tmtt.2022.3184018

Massive MIMO Precoding Methods That Minimize the Variation in Average Power and Active Impedance With Channel Conditions

2022· article· en· W4285283743 on OpenAlexaff
Mohammed Almoneer, Jin Gyu Lim, Hang Yu, Patrick Mitran, Slim Boumaiza

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPrecodingTransmitterZero-forcing precodingMIMOElectronic engineeringAntenna (radio)Computer scienceControl theory (sociology)Electrical impedanceChannel (broadcasting)EngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

This article investigates the impact of precoding on the performance of massive multiple-input multiple-output (mMIMO) transmitters exhibiting nonnegligible antenna crosstalk. For this, a new metric, called the average active impedance, is introduced to quantify the extent of load modulation as a function of the antenna S-parameters and the employed precoding. The new metric is then used to investigate the load modulation under conventional precoding schemes. It is shown that these precoders yield large disparities in average-power levels across the radio frequency (RF) chains, which results in substantial performance variations with channel conditions. Based on this investigation, we propose three new precoding schemes that yield equal average-power (EP) levels across all RF chains, independent of the channel conditions. Numerical simulations and experiments conducted on an mMIMO transmitter prototype confirmed that the proposed schemes improve the transmitter’s resilience to the variation in channel conditions.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.253
Teacher spread0.243 · 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 designBench or experimental
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

Citations9
Published2022
Admission routes1
Has abstractyes

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