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Record W1556119955 · doi:10.1109/glocom.1994.513165

Performance improvement in a multihop packet radio network using multiple antennas

2002· article· en· W1556119955 on OpenAlex
Vincent W. S. Wong, Cyril Leung

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRayleigh fadingComputer scienceComputer networkTransmission (telecommunications)Packet radioDirectional antennaNetwork packetNode (physics)Antenna (radio)FadingRadio Link ProtocolElectronic engineeringTelecommunicationsWirelessEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

Studies the performance improvement brought by the use of multiple antennas for each node in a multihop packet radio network (PRN). The authors propose that the antenna with the largest received power is chosen to be decoded. Four transmission strategies based on the MFR (most forward with fixed range), NFP (nearest with forward progress), MAD (minimal angular deviation), and ARR (angular deviation to transmission range ratio) routing schemes are studied. Results show that more than one antenna for each node in a PRN can improve the performance of all four transmission strategies substantially in a Rayleigh fading environment.

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: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.600

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 head0.025
GPT teacher head0.217
Teacher spread0.191 · 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

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

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