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Record W4386973824 · doi:10.1109/lwc.2023.3318432

Performance of OTFS-NOMA Scheme for Coordinated Direct and Relay Transmission Networks in High-Mobility Scenarios

2023· article· en· W4386973824 on OpenAlexaff
Yao Xu, Zhen Du, Weijie Yuan, Shaobo Jia, Victor C. M. Leung

Bibliographic record

VenueIEEE Wireless Communications Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of British Columbia
FundersStartup Foundation for Introducing Talent of Nanjing University of Information Science and TechnologyNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsComputer scienceNomaRelayTransmission (telecommunications)Topology (electrical circuits)Outage probabilityInterference (communication)Transformation (genetics)Electronic engineeringAlgorithmComputer networkDecoding methodsMathematicsTelecommunicationsTelecommunications linkEngineeringFadingPower (physics)Channel (broadcasting)Physics

Abstract

fetched live from OpenAlex

This letter proposes an orthogonal time frequency space (OTFS) modulation based non-orthogonal multiple access (NOMA) scheme for the coordinated direct and relay transmission system, where a source communicates directly with a nearby mobile user, and serves a distant user with high-mobility relying on the relaying assistance. However, the coexistence of signal superposition coding and multi-domain transformation presents a challenge in measuring the performance of OTFS-based NOMA from a theoretical perspective. To accurately evaluate the system performance of the proposed scheme, we derive the closed-form expressions for the outage probability and outage sum rate using the inversion formula and characteristic function under both perfect SIC and imperfect SIC. Numerical results verify the performance superiority and the effectiveness of the proposed scheme.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations12
Published2023
Admission routes1
Has abstractyes

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