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Record W2170678650 · doi:10.1109/tcomm.2012.12.100671

Partial and Opportunistic Relay Selection with Outdated Channel Estimates

2012· article· en· W2170678650 on OpenAlexaff
Madushanka Soysa, Himal A. Suraweera, Chintha Tellambura, Hari Krishna Garg

Bibliographic record

VenueIEEE Transactions on Communications · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelayRelay channelChannel state informationChannel (broadcasting)Selection (genetic algorithm)Computer scienceSignal-to-noise ratio (imaging)Scheduling (production processes)Outage probabilityBit error rateControl theory (sociology)TelecommunicationsMathematicsFadingWirelessMathematical optimizationPower (physics)Physics

Abstract

fetched live from OpenAlex

This paper investigates the impact of using outdated channel estimates for relay selection and signal amplification on the performance of amplify-and-forward (AF) relays under partial relay selection (PRS) and opportunistic relay selection (ORS). In practice, outdated channel state information (CSI) can occur due to feedback or scheduling delay. Both variable gain (VG) AF and fixed gain AF schemes are considered. Outage probability, the average bit error rate (BER) and simplified high signal-to-noise ratio approximations are derived. The effect of parameters such as the number of relays, the rank of chosen relay, and the correlation between the delayed and current channel state information are analyzed. Outdated CSI for computing relay gains in PRS causes about 2 dB loss. In ORS, a 3% reduction in correlation causes up to an order of magnitude increase in the outage probability.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
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 head0.079
GPT teacher head0.302
Teacher spread0.223 · 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 designTheoretical or conceptual
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

Citations115
Published2012
Admission routes1
Has abstractyes

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