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Record W1910450741 · doi:10.1109/icupc.1995.497133

A cost-effective PCS deployment methodology

2002· article· en· W1910450741 on OpenAlexaff
Saleh Faruque, M. Maragoudakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsSoftware deploymentPath lossMultipath propagationComputer scienceAntenna (radio)Radio propagationPower (physics)Point (geometry)Process (computing)Electronic engineeringTelecommunicationsWirelessEngineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

RF propagation in multipath environment is random, requiring sophisticated computer aided prediction tools. As a result, traditional methods of RF deployment for low power/low antenna height sites are generally expensive. In an effort to alleviate this problem, this paper presents a simpler and cost-effective deployment method for PCS. The salient concept of the proposed method is to maintain cell radii within the fresnel zone break point. In this region, the path loss slope generally remains constant irrespective of the propagation medium. This analogy is then used to develop a three-ray model for indoor PCS; a two-ray model is then derived for outdoor PCS. Empirical formulae are presented for cell radii and the associated link budget parameters. The proposed method is appropriate for low power PCS and microcellular services, offering a greatly simplified and cost-effective deployment process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.088
GPT teacher head0.304
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2002
Admission routes1
Has abstractyes

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