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Record W1949164554 · doi:10.1109/aps.2002.1017070

An accurate and effective physical layer simulator for micro- and pico-cellular radio systems and networks

2003· article· en· W1949164554 on OpenAlexaff
Cutberto A. Santillan, S. Safavi‐Naeini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceBluetoothPhysical layerRadio propagationBasebandMultipath propagationRadio propagation modelAir interfacePath lossRadio Link ProtocolBroadband networksComputer networkElectronic engineeringBroadbandWirelessInterference (communication)MicrocellChannel (broadcasting)TelecommunicationsEngineeringBandwidth (computing)

Abstract

fetched live from OpenAlex

Complex multipath and interference effects have become the important system design and planning considerations in micro- and pico-cellular broadband mobile data networks such as wireless LAN (WLAN) and very short range personal area networks (PAN) like those based on Bluetooth. Another important issue is the interoperability and coexistence of these various network technologies. Bluetooth and IEEE 802.11b use the same portion of radio spectrum (2.4 GHz) and are liable to interfere. Most of the recent propagation models are based on simple statistical path loss algorithms, which become very inaccurate in the complex indoor environment. The deals with an accurate indoor end-to-end physical layer (radio channel) model including the radiowave propagation environment, antenna model, and the RF/IF front end circuit and baseband processes. The simulator's interface geometrical modeler makes the tool very convenient for complex environments. Results are shown indoor propagation effects for short-range technologies interfering on each other in a simple common RF link.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.407

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 head0.013
GPT teacher head0.256
Teacher spread0.242 · 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
GenreEmpirical

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
Published2003
Admission routes1
Has abstractyes

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