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Record W2690388780 · doi:10.1109/ccece.2017.7946671

Joint power control and beamformer design with antenna selection

2017· article· en· W2690388780 on OpenAlexaff
Faika Hoque, Yvon Savaria, Christian Cardinal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBeamformingPower controlTelecommunications linkComputer scienceAntenna (radio)Computational complexity theoryBase stationTransmitter power outputPower (physics)Selection (genetic algorithm)Antenna arrayPower optimizationSelection algorithmElectronic engineeringControl theory (sociology)EngineeringTelecommunicationsAlgorithmControl (management)TransmitterArtificial intelligence

Abstract

fetched live from OpenAlex

Joint power control with optimal beamforming design and power control with minimum antenna selection under signal to inference noise ratio constraints have been considered in separate wireless communication scenarios. In power control algorithms, multiple antennas with linear beamforming are used to reduce the interference power, which also minimizes the total transmitted power in uplink/downlink channels, while the minimum antenna selection problem is solved to reduce the computational complexity at the base stations as well as to reduce the transmits power within a group of users. In this paper, we jointly consider the power control and optimal beamforming design algorithm with limited number of best antenna set selection in uplink communication. Simulation results show that the power control algorithm combined with the best number of antenna selection method can minimize power. Consequently, the proposed algorithm can reduce significantly the computational complexity while retaining the benefits that stem from solving the sum of transmitted power minimization problem.

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: Methods · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.582

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.039
GPT teacher head0.275
Teacher spread0.237 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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