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Record W4361759623 · doi:10.1109/lnet.2023.3262704

EDI-Driven Multi-Dimensional Resource Allocation for Inclusive 6G Communications

2023· article· en· W4361759623 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

VenueIEEE Networking Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceProvisioningQuality of serviceResource distributionResource allocationEquity (law)Scheme (mathematics)Computer networkDistributed computingMathematics

Abstract

fetched live from OpenAlex

This letter proposes a novel inclusive multi-dimensional resource allocation scheme for diverse 6G communications driven by the principle of Equity, Diversity, and Inclusion (EDI). In coping with challenges of ever-increasing device diversity and application Quality-of-Service (QoS) heterogeneity, the proposed scheme can inclusively support all users with equitable service provisioning satisfaction level and flexible multiple access. By jointly considering user-specific communication requirements and scenario-dependent resource utilization cost, we aim to maximize the average user utility while minimizing its distribution variance. Simulation demonstrates the proposed scheme can inclusively provide equitable user QoS satisfactions by higher average utility with smaller disparity and inequality.

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 score0.867
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.288
Teacher spread0.247 · 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