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SER Analysis and PDF Derivation for Multi-Hop Amplify-and-Forward Relay Systems

2010· article· en· W2126252670 on OpenAlexaff
Chris Conne, MinChul Ju, Zhihang Yi, Hyoung‐Kyu Song, Il‐Min Kim

Bibliographic record

VenueIEEE Transactions on Communications · 2010
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsQueen's University
Fundersnot available
KeywordsMoment-generating functionRelayHop (telecommunications)Random variableCumulative distribution functionProbability density functionErlang (programming language)Expression (computer science)MathematicsTopology (electrical circuits)Probability of errorBit error rateComputer scienceAlgorithmTelecommunicationsStatisticsCombinatoricsPhysicsDecoding methodsTheoretical computer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

An amplify-and-forward, multi-branch, multi-hop relay system with K relays, in which the relays broadcast to other relays as well as the destination, is analyzed. An approximate symbol-error-rate (SER) expression, which is valid for any number of relays and for several modulation schemes, is found for the multi-hop system. Also, the cumulative density function (CDF) and probability density function (PDF) are found for the random variable, Z = XY/(X + Y + c), where X and Y are sums of independent, Erlang random variables, and c is a constant. The moment generating function (MGF) of Z is found for the special case in which c = 0. It is shown that these results are generalizations of previously published results for special cases of Z. The MGF of Z is used to develop the approximate SER expression. Results for the analytic SER expression are included and compared with simulation results for various values of K, for various modulation schemes, and for two choices of system parameters (channel variances). Results for the multi-hop system are also compared to results for the two-hop system (in which relays transmit only to the destination).

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 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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.321
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations19
Published2010
Admission routes1
Has abstractyes

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