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Record W2144903671 · doi:10.1109/issse.2007.4294512

Adaptive Microwave Beamforming with Low Weighting Rate Requirement

2007· article· en· W2144903671 on OpenAlexaff
Abdel-Razik Sebak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsBeamformingWeightingAdaptive beamformerBandwidth (computing)Computer scienceMicrowaveSIGNAL (programming language)Signal-to-interference-plus-noise ratioInterference (communication)AlgorithmElectronic engineeringControl theory (sociology)AcousticsTelecommunicationsPhysicsEngineeringPower (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

A new structure for single-port adaptive microwave beamforming is proposed. In this structure the array signal vector is estimated for gradient vector estimation based on temporally-correlated array signal samples. The temporal correlation is provided by lowering the overall receiver bandwidth instead of high weighting rate which makes the structure realizable with slower control elements. The structure is simulated in time domain including the beam switching transients. Adaptive constrained least mean square (CLMS) algorithm is implemented with the proposed structure. It is observed that the proposed single-port structure achieves higher signal to interference plus noise ratio (SINR) gain than the corresponding multi-port structure.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.836
Threshold uncertainty score0.326

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 communication0.0000.001
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 head0.014
GPT teacher head0.251
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 designBench or experimental
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

Citations0
Published2007
Admission routes1
Has abstractyes

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