MétaCan
Menu
Back to cohort
Record W4285264033 · doi:10.1109/lcomm.2022.3186795

Hybrid Multiple Access Transmission in Satellite-Aerial-Terrestrial Networks

2022· article· en· W4285264033 on OpenAlexaff
Huaicong Kong, Miaomiao Tan, Min Lin, Ming Cheng, Wei‐Ping Zhu, Tomaso de Cola

Bibliographic record

VenueIEEE Communications Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsTelecommunications linkComputer scienceSpace-division multiple accessComputer networkSingle antenna interference cancellationSatelliteTransmission (telecommunications)Communications satelliteMonte Carlo methodInterference (communication)Random accessScheme (mathematics)Real-time computingTelecommunicationsEngineeringMathematicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

This letter proposes a hybrid multiple access (HMA) scheme to provide reliable connectivity for heterogeneous users in a satellite-aerial-terrestrial network (SATN). Here, multiple internet-of-things devices access a multi-antenna aerial platform through space division multiple access (SDMA) technology while the aerial platform and the earth station communicate with the satellite using uplink non-orthogonal multiple access (NOMA). Considering perfect and imperfect successive interference cancellation (SIC) at the satellite, we derive the closed-form ergodic sum rate (ESR) expression for the uplink transmission with the proposed HMA scheme, which is verified through Monte Carlo simulations. Besides, the priority of the proposed scheme is also demonstrated to some benchmarks in terms of ESR.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.278
Teacher spread0.226 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Communications LettersSame topicSatellite Communication SystemsFrench-language works237,207