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Record W2066560648 · doi:10.1109/icc.2010.5502099

Exact Closed-Form Error Probability Expression for Cooperative Diversity Networks with Channel Estimation Errors in Time Selective Rayleigh Fading Channels

2010· article· en· W2066560648 on OpenAlexaff
Salama Ikki, Suhail Al–Dharrab, Murat Uysal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRayleigh fadingFadingChannel (broadcasting)Computer scienceBit error rateMaximal-ratio combiningExpression (computer science)Closed-form expressionAlgorithmChannel state informationDiversity combiningPhase-shift keyingModulation (music)Electronic engineeringTopology (electrical circuits)TelecommunicationsMathematicsWirelessEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, we investigate the performance of a cooperative network with adaptive decode-and-forward (DF) relaying over time-selective frequency-flat Rayleigh fading channels. In adaptive DF relaying, only a subset of the available relays with "good" channels are allowed to participate in the relaying phase. The destination combines the direct and the relayed signals using maximal ratio combining technique. Pilot-symbol-assisted modulation (PSAM) is used for the estimation of time-varying fading channel coefficients. For the system under consideration, we derive an exact closed-form expression for the average bit error rate. Such closed form solutions are highly desirable because they allow for rapid and efficient evaluation of system performance. We further present computer simulations to validate our analytical results.

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)0.005
metaresearch head score (Gemma)0.023
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.269
Teacher spread0.237 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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