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Record W2753344520 · doi:10.1109/tcsii.2017.2749377

Spectral Efficiency Maximization of Multiuser Massive MIMO Systems With Finite-Dimensional Channel via Control of Users’ Power

2017· article· en· W2753344520 on OpenAlexaff
O. Saatlou, M. Omair Ahmad, M.N.S. Swamy

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsSpectral efficiencyTelecommunications linkMIMOMaximizationFadingUpper and lower boundsTransmitter power outputBase stationChannel (broadcasting)Computer sciencePower controlTransmission (telecommunications)Power (physics)Constraint (computer-aided design)Control theory (sociology)Antenna (radio)MathematicsAlgorithmMathematical optimizationTelecommunicationsControl (management)PhysicsTransmitter

Abstract

fetched live from OpenAlex

This brief investigates the spectral efficiency of multiuser massive multiple-input multiple-output systems with a very large number of antennas at a base station serving single-antenna users. A practical physical channel model is proposed by dividing the angular domain into a finite number of distinct directions. A lower bound on the achievable rate of uplink data transmission is derived using a linear detector for each user and employed in defining the spectral efficiency. The obtained lower bound is further modified for the maximum-ratio combining and zero-forcing receivers. A power control scheme based on the large-scale fading is also proposed to maximize the spectral efficiency under peak power constraint. Experiments are conducted to evaluate the obtained lower bounds and the performance of the proposed method. The numerical results show a comparison between the performances of both receivers along with the advantage of the proposed power control scheme in terms of the spectral efficiency.

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 categoriesMeta-epidemiology (narrow)
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 score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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.

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

Citations16
Published2017
Admission routes1
Has abstractyes

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