MétaCan
Menu
Back to cohort
Record W2163693360 · doi:10.1109/vtcf.2006.196

Broadband Wireless Access Interference and Capacity Estimation

2006· article· en· W2163693360 on OpenAlexaff
Regis Lerbour, Tolga Kurt, Yann Le Helloco, B. Breton

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsEricsson (Canada)
Fundersnot available
KeywordsComputer scienceComputer networkOrthogonal frequency-division multiplexingInterference (communication)Broadband networksThroughputTelecommunications linkWireless broadbandBase stationSoft handoverTransmitter power outputWireless networkBroadbandOrthogonal frequency-division multiple accessFrequency-division multiple accessWirelessTelecommunicationsTransmitterChannel (broadcasting)

Abstract

fetched live from OpenAlex

The performance of an IEEE 802.16 based OFDMA network is simulated employing real geographical and base station data. The effects of soft handover, smart antennas, and transmit diversity on system performance are presented. In particular, the relationship between power control and adaptive modulation is investigated and shown to reduce the uplink interference considerably, thus improving the coverage and the capacity of the network. The differences between the interference characteristics of OFDM and OFDMA based operations are also presented. The reasons for dropped calls and a throughput analysis for a typical OFDMA network are also discussed.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Vehicular Technology ConferenceSame topicAdvanced Wireless Network OptimizationFrench-language works237,207