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Record W4406094368 · doi:10.1109/lwc.2025.3526756

Performance Analysis of Rate-Splitting Multiple Access Technique in Impulsive Noise Environments

2025· article· en· W4406094368 on OpenAlexaff
Hazem Barka, Md Sahabul Alam, Georges Kaddoum, Minh Au, Naveed Iqbal

Bibliographic record

VenueIEEE Wireless Communications Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceNoise (video)Computer networkTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Rate-splitting multiple access (RSMA) has emerged as a promising multiple access paradigm in wireless communications, offering improved spectral efficiency and user fairness. Despite its advantages, the performance of RSMA in impulsive noise channels has not been investigated. To bridge this gap, we derive exact closed-form expressions for the outage probability (OP) in RSMA schemes over Rayleigh fading channels. The accuracy of the analytical results is verified through numerical simulation. Finally, performance comparison with non-orthogonal multiple access demonstrates that RSMA exhibits superior interference resilience in downlink scenarios, whereas both schemes experience considerable performance degradation in the uplink scenario.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.015
GPT teacher head0.267
Teacher spread0.251 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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