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Record W2027902893 · doi:10.1109/pimrc.2010.5671951

Cognitive uplink interference management in 4G cellular femtocells

2010· article· en· W2027902893 on OpenAlex

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
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFemtocellMacrocellTelecommunications linkComputer networkComputer scienceInterference (communication)Base stationChannel (broadcasting)Cognitive radioScheduling (production processes)TelecommunicationsWirelessEngineering

Abstract

fetched live from OpenAlex

We study a cognitive channel reuse approach for autonomous femtocell deployment in an OFDMA-based cellular network. The objective is to manage the uplink interference from macrocell users to femtocell access points. Macrocell users are normally far away from the base station and may cause strong uplink interference to the close-by femtocells. By cognitive interference recognition, a femtocell access point autonomously reuse the proper channel modes for interference minimization. We propose an opportunistic channel scheduling scheme that determines optimal channel and power allocation to femtocell users. Simulation results show improved uplink performance in terms of the average channel outage probability in femtocells.

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: none
Teacher disagreement score0.940
Threshold uncertainty score0.416

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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