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Record W2419144360 · doi:10.1109/wts.2016.7482052

Resource management in OFDMA heterogeneous network

2016· article· en· W2419144360 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 Wireless Network Optimization
Canadian institutionsKensington Health
FundersEuropean Social FundNational Technical University of AthensEuropean Commission
KeywordsOrthogonal frequency-division multiple accessComputer scienceChannel state informationResource allocationResource management (computing)Interference (communication)Frequency-division multiple accessRadio resource managementHeterogeneous networkComputer networkOrthogonal frequency-division multiplexingChannel (broadcasting)Distributed computingTelecommunicationsWireless networkWireless

Abstract

fetched live from OpenAlex

In this study, a Long Term Evolution Advanced (LTEa) - based multi-user Orthogonal Frequency Division Multiple Access (OFDMA) heterogeneous network has been simulated and a resource allocation strategy has been proposed. The strategy under consideration can inherently mitigate electromagnetic interference, hence increases the mean number of terminals, and requires no channel state information (CSI). To evaluate the performance of the network platform and the proposed strategy, the system is studied for different network orientations. According to the results, the platform is a good reality simulator, whereas owning to the proposed Radio Resource Management (RRM) algorithm the mean capacity can reach a 12-fold increase especially for highly noisy operating environments.

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 score0.959
Threshold uncertainty score0.249

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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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