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Record W1980860339 · doi:10.1109/iccw.2013.6649419

OFCDM-based small femtocells embedded in OFDM-based macro cellular network

2013· article· en· W1980860339 on OpenAlexaff
Fatima Hussain, Alagan Anpalagan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFemtocellComputer scienceMacroOrthogonal frequency-division multiplexingFemto-Computer networkCellular networkFadingInterference (communication)Electronic engineeringEngineeringBase stationChannel (broadcasting)

Abstract

fetched live from OpenAlex

To extend indoor coverage and offload cellular network traffic, femtocell technology becomes more promising. Multi-carrier spread spectrum technology has proven to be effective in multiple access and provides robust performance in fading radio channels. In this paper, VSF-OFCDM is utilized for sub-carrier allocation with two dimensional spreading (2D) for femto users (FUEs) in small cells that are deployed within an OFDMA macro network. The performance of femto/macro hybrid network is investigated by deriving closed form expressions for the SINR for FUEs and macro users (MUEs) in uplinks, that are impacted by different types of interferers, 2D spreading factors and load conditions. We also evaluate the BER performance of FUEs and MUEs through Monte Carlo simulation in interference- and noise-limited scenarios for different parameters.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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.

Study designSimulation or modeling
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

Citations2
Published2013
Admission routes1
Has abstractyes

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