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Record W2154535919 · doi:10.1109/vtcf.2006.296

Adaptive Kalman Filtering for Local Mean Power Estimation in Mobile Communications

2006· article· en· W2154535919 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsEricsson (Canada)
Fundersnot available
KeywordsKalman filterFast Kalman filterComputer scienceExtended Kalman filterInvariant extended Kalman filterAdaptive filterWirelessPower (physics)Control theory (sociology)Artificial intelligenceAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we investigate the local mean signal estimation problem for wireless communications. Recently, it has been shown that the Kalman filtering approach results in better power estimates when compared to the traditional window based approaches for mean power estimation. In our work, we extend the Kalman filtering approach to adaptive Kalman filtering by combining the Kalman filtering with the window based filtering. Hence, Kalman filtering became available for practical use. The filter has been tested with field measurements and shown to outperform window based techniques.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.679

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 science0.0010.000
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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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