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Record W2030820691 · doi:10.1109/icc.2010.5502353

Demodulate-and-Forward Relaying with Higher Order Modulations: Impact of Channel State Uncertainty

2010· article· en· W2030820691 on OpenAlexaff
Ramesh Annavajjala, Amine Maaref, J. Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsDemodulationQuadrature amplitude modulationRayleigh fadingQAMRelayComputer scienceIndependent and identically distributed random variablesFadingChannel state informationPulse-amplitude modulationModulation (music)Bit error rateTelecommunicationsAlgorithmTopology (electrical circuits)Channel (broadcasting)MathematicsElectronic engineeringStatisticsWirelessPhysicsDetectorPulse (music)EngineeringRandom variable

Abstract

fetched live from OpenAlex

In this paper, we study the impact of uncertain channel state information (CSI) on the performance of demodulate-and-forward relaying protocols with higher order modulation formats such as pulse-amplitude modulation (PAM) and rectangular quadrature-amplitude modulation (QAM). Assuming a single source and a single destination node assisted by $N$ relay nodes, we study the average bit error probability (BEP) performance of $M$-ary PAM and rectangular QAM constellations with Gray code mapping and imperfect CSI at the relay nodes as well as the destination. The main contributions of this paper are the derivation of closed-form expressions for $a)$ the cumulative distribution functions of the demodulator test statistics, $b)$ the transition probability of error at a given relay, and $c)$ the average BEP for independent and not necessarily identically distributed Rayleigh fading channels with imperfect receiver CSI.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.282
Teacher spread0.260 · 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
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

Citations14
Published2010
Admission routes1
Has abstractyes

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