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Record W2165795763 · doi:10.1109/icc.1997.604947

A fast algorithm for computerized indoor radio channel estimation

2002· article· en· W2165795763 on OpenAlexaff
M. Kimpe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceAlgorithmRay tracing (physics)Base stationFloor planChannel (broadcasting)Impulse (physics)Set (abstract data type)Radio channelReal-time computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Real time computer tools are being researched in telecommunications to assess the coverage of the various antennas of a cellular network. Given the floor plan of a building, those computer tools simulate the behavior of the electromagnetic waves using ray tracing techniques in order to determine the quality of the signal reception in the regions covered by the various antennas of the system. Real time capabilities have not yet been achieved. Therefore fast algorithms for this application could be of considerable interest. This paper describes a fast 2-D algorithm to compute impulse responses between one base station location and a set of receiver locations. The performance gain over the standard method of images algorithm is assessed for two types of door plans. The possible implementation of a portion of the algorithm on a parallel machine is discussed. The algorithm can be extended to 3-D.

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: Methods
Teacher disagreement score0.880
Threshold uncertainty score0.356

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 head0.027
GPT teacher head0.213
Teacher spread0.186 · 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
Published2002
Admission routes1
Has abstractyes

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