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Record W3184419608 · doi:10.1109/lwc.2021.3099302

Symbol Error Analysis of Amplify-and-Forward Based Multiuser Hybrid Satellite-Terrestrial Relay Network

2021· article· en· W3184419608 on OpenAlex
Zhongyuan Zhao, Guanjun Xu, Qinyu Zhang

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 Wireless Communications Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsRelayComputer scienceCommunications satelliteSignal-to-noise ratio (imaging)Bit error rateSymbol rateScheduling (production processes)Channel (broadcasting)SatelliteComputer networkTelecommunicationsMathematicsMathematical optimizationPower (physics)Engineering

Abstract

fetched live from OpenAlex

In this letter, we study the average symbol error rate (ASER) performance in a multiuser hybrid satellite-terrestrial relay network (HSTRN) with opportunistic scheduling. The signal broadcast from the satellite is amplified by relay and forwarded to the terrestrial user that has the maximum instantaneous signal-to-noise ratio (SNR) in its channel. Specifically, both the approximate analytical formula and asymptotic expression at high SNRs of the ASER are derived for the fixed gain relaying as well as the variable gain relaying. Monte Carlo simulation are provided to verify our analysis and indicate the performance disparity between the two kinds of relaying protocols.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.269
Teacher spread0.233 · 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