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Record W3024562595 · doi:10.1186/s13638-020-01705-5

Performance bounds for diversity receptions over a new fading model with arbitrary branch correlation

2020· article· en· W3024562595 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

VenueEURASIP Journal on Wireless Communications and Networking · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsFadingFading distributionChannel state informationAlgorithmNakagami distributionComputer scienceLine-of-sightDiversity combiningMaximal-ratio combiningStatisticsMathematicsTelecommunicationsChannel (broadcasting)WirelessPhysicsRayleigh fading

Abstract

fetched live from OpenAlex

Abstract The performance of a new (Beaulieu-Xie) fading model is analyzed using bounds. This recently proposed fading model can be used to describe both line-of-sight and non-line-of-sight components of a fading channel having different diversity orders. We consider the outage probability and error rate performance of maximal ratio combining, equal-gain combining, and selection combining over arbitrarily correlated Beaulieu-Xie fading channels. Closed-form expressions for upper and lower bounds to the outage probability and error rate are obtained, and it is shown that these bounds are asymptotically tight in the high signal-to-noise ratio regime. The analytical results are verified via Monte Carlo simulations. It is shown that the Beaulieu-Xie fading model can be more useful than the Ricean and Nakagami- m fading models in characterizing environments with both line-of-sight and multiple reflected specular components.

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 score0.865
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.242
Teacher spread0.199 · 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