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Record W1980315494 · doi:10.1109/tvt.2012.2184311

Performance Analysis of Fixed Gain Relay Systems With a Single Interferer in Nakagami- $m$ Fading Channels

2012· article· en· W1980315494 on OpenAlexaff
Himal A. Suraweera, Diomidis S. Michalopoulos, Chau Yuen

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNakagami distributionFadingRelayInterference (communication)Bit error rateSignal-to-noise ratio (imaging)Outage probabilityCo-channel interferenceComputer scienceTopology (electrical circuits)Electronic engineeringMathematicsTelecommunicationsElectrical engineeringPhysicsEngineeringChannel (broadcasting)Power (physics)

Abstract

fetched live from OpenAlex

We investigate the outage probability and average bit error rate (BER) of a dual-hop fixed gain relaying system in the presence of interference and noise at the relay and destination. Our analysis assumes Nakagami-m fading for the source-relay, relay-destination, and interfering channels. We present new closed-form/series expressions for the outage probability, as well as the average BER. It is concluded that the presence of interference results in a floor point in both outage and BER performance. This floor point holds for signal-to-noise ratio (SNR) values higher than a certain threshold such that the larger the difference between the received powers of the useful and interfering signals, the higher the SNR value at which this floor point commences. Moreover, it is shown that the performance is practically not affected by the Nakagami-m shape parameter of the fading at the interfering link.

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)0.006
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.244
Teacher spread0.217 · 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

Citations64
Published2012
Admission routes1
Has abstractyes

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