MétaCan
Menu
Back to cohort
Record W2510122326 · doi:10.1109/spawc.2016.7536897

Modified MRT and outage probability analysis for massive MIMO downlink under per-antenna power constraint

2016· article· en· W2510122326 on OpenAlexaff
Chi Feng, Yindi Jing

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPrecodingTelecommunications linkMIMOConstraint (computer-aided design)Computer scienceZero-forcing precodingTransmitter power outputAntenna (radio)Interference (communication)Probability density functionPower (physics)Signal-to-noise ratio (imaging)Signal-to-interference-plus-noise ratioElectronic engineeringMathematical optimizationControl theory (sociology)AlgorithmMathematicsTelecommunicationsTransmitterStatisticsBeamformingEngineeringPhysics

Abstract

fetched live from OpenAlex

For single-cell multi-user massive multi-input-multi-output (MIMO) system downlink, we consider the per-antenna power constraint and propose a modified maximum-ratio-transmission (MRT) precoding scheme. The outage probability performance of the scheme are analyzed. To enable the analysis, we study the random behaviour of the signal-to-interference-plus-noise-ratio (SINR) and derive an approximation for the probability density function (pdf) of the interference power. Simulation results are shown to validate the theoretical derivations. Moreover, our work show that the massive MIMO system with per-antenna power constraint under the modified MRT precoding can achieve lower outage probability than that with a total power constraint under the standard MRT precoding, even though its power constraint is more strict.

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: Methods · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.461

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced MIMO Systems OptimizationFrench-language works237,207