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Record W4285120405 · doi:10.1109/tvt.2022.3186871

Effective Capacity Analysis of AmBC-NOMA Communication Systems

2022· article· en· W4285120405 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 Transactions on Vehicular Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversité Laval
FundersHenan Provincial Science and Technology Research Project
KeywordsNomaComputer scienceChannel capacityElectronic engineeringComputer networkEngineeringTelecommunications link

Abstract

fetched live from OpenAlex

In this paper, we propose an ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) system framework and derive exact expressions for the effective capacity (EC) of two NOMA users and the backscatter device (BD). For more insights, we provide asymptotic analysis by invoking <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high signal-to-noise ratio (SNR) slope</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high SNR power offset</i> . The simulation indicates that: 1) the ECs of users tend to be the constants in the high SNR region while the EC of BD increases linearly with SNR; 2) comparing the ECs of two users, the EC advantage of near user is more obvious when QoS constraint is loose; 3) the proposed system yields better user fairness than the orthogonal multiple access case; 4) the EC of BD strengthens with the increase of BD’s reflection coefficient, while the ECs of NOMA users weaken.

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 score0.553
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.215
Teacher spread0.206 · 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