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Record W2890807948 · doi:10.1109/lcomm.2018.2870425

Distributed Resource Allocation for IBFD-Enabled NOMA Systems

2018· article· en· W2890807948 on OpenAlexaff
Rui Tang, Hua Qu, Jihong Zhao, Julian Cheng, Zhaoxin Cao

Bibliographic record

VenueIEEE Communications Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsNomaComputer scienceResource allocationComputer networkResource management (computing)Resource (disambiguation)Distributed computingTelecommunications link

Abstract

fetched live from OpenAlex

A joint power control and channel assignment problem for an in-band full duplex (IBFD)-enabled non-orthogonal multiple access (NOMA) system is investigated. To strike a balance between performance and complexity, a matching-based distributed channel assignment mechanism is proposed based on the optimal design of power control, and it achieves a stable Pareto-optimal solution to the resulting combinatorial optimization problem in polynomial time. Simulations are conducted to show the efficiency of the proposed mechanism and the benefits when IBFD and NOMA are integrated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.832

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 science0.0020.000
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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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