MétaCan
Menu
Back to cohort

Outage Probability of Decode-and-Forward Relaying with Optimum Combining in the Presence of Co-Channel Interference and Nakagami Fading

2013· article· en· W2075177459 on OpenAlex
Navod Suraweera, Norman C. Beaulieu

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 · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNakagami distributionFadingCo-channel interferenceInterference (communication)Maximal-ratio combiningDiversity gainRelayNode (physics)Topology (electrical circuits)Outage probabilityComputer scienceChannel (broadcasting)Diversity combiningSignal-to-noise ratio (imaging)TelecommunicationsComputer networkMathematicsPhysicsPower (physics)CombinatoricsAcoustics

Abstract

fetched live from OpenAlex

The performance of optimum combining (OC) used in a decode-and-forward relay network over Nakagami-m fading channels in the presence of co-channel interference at the relay nodes and at the destination is analyzed. A closed-form expression is derived for the exact outage probability. It is found that OC cannot be used to achieve end-to-end diversity gain when interference is present at single-antenna relays, but the outage probability floor at the destination receiver is lowered by the OC. If the interference is present only at the destination, diversity gains can be achieved using OC. The performance of OC is compared with maximal-ratio combining (MRC) and OC achieves diversity gain if interference is present only at the destination node, whereas MRC does not.

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 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 score0.922
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.049
GPT teacher head0.280
Teacher spread0.232 · 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