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Record W2016649359 · doi:10.1109/wcnc.2014.6952194

Doppler compensation for D-STBC coded time-varying underwater acoustic channels

2014· article· en· W2016649359 on OpenAlexaff
Saed Daoud, Bahattin Karakaya, Ali Ghrayeb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingComputer scienceSpace–time block codeElectronic engineeringDoppler effectUnderwater acoustic communicationTelecommunicationsUnderwaterChannel (broadcasting)EngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper we investigate the performance of distributed space-time block coding (D-STBC) orthogonal frequency division multiplexing (OFDM) over underwater acoustic (UWA) channels. In particular, we consider a relaying system consisting of one source, two relays, and one destination. The relays operate in amplify-and-forward (AF) mode. The underlying channels are assumed to be time-varying frequency selective channels, where the only source of time variation is the relative motion between transceivers. Alamouti D-STBC scheme is used in the second hop, and a two stage receiver is adopted at the destination: in the first stage, multiple resampling (MR) preprocessing of the received signals is performed to minimize the effect of intercarrier interference (ICI), and in the second stage ICI equalization is performed in the frequency domain to further reduce the effect of the residual ICI. To further boost the performance, successive interference cancellation (SIC) is used, where the estimates of the signals at the output of the ICI equalizer are used as tentative decisions. Compared to the single resampling (SR) front end preprocessing, simulation results show the superiority of MR front-end receiver. Also, SIC further boosts the performance, but still, there is a significant gap with respect to the ICI-free limit, when the receiver has perfect knowledge of ICI coefficients and eliminates them completely.

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: none
Teacher disagreement score0.979
Threshold uncertainty score0.432

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 head0.226
Teacher spread0.203 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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