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Record W2093444982 · doi:10.1049/iet-com.2012.0234

Bit‐error‐rate performance improvement of mobile dual‐hop relaying systems using directional antennas

2013· article· en· W2093444982 on OpenAlexafffund
Hassan A. Ahmed, Ahmed Iyanda Sulyman, Hossam S. Hassanein

Bibliographic record

VenueIET Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsQueen's University
FundersNational Plan for Science, Technology and InnovationNatural Sciences and Engineering Research Council of CanadaKing Saud University
KeywordsBeamwidthBit error rateComputer scienceOrthogonal frequency-division multiplexingRelayElectronic engineeringDoppler effectAntenna (radio)TelecommunicationsPhysicsEngineeringDecoding methods

Abstract

fetched live from OpenAlex

The bit‐error‐rate (BER) performance of orthogonal frequency division multiplexing (OFDM) systems in mobile multi‐hop relaying (MMR) system is severely degraded by the effect of Doppler shift and the severity of this degradation increases with the number of hops traversed by the OFDM signal. In this study, the authors propose a method to mitigate the effect of Doppler shift in MMR system (such as the IEEE 802.16j system) using directional antennas. It is shown that the effect of the resulting inter‐carrier interference (ICI) because of the phase noise generated by the Doppler shift over multi‐hop relaying channels, can be reduced by employing directional antennas at both the mobile and relay stations. Consequently, the BER performance of MMR system is significantly enhanced. Analysis and simulation results show that the BER enhancements using the proposed approach are strongly related to the orientation and beamwidth of the directional antenna employed. As the antenna beamwidth is reduced, the BER enhancement increases for both the perpendicular and parallel antenna orientations, and comparing these two orientations, the parallel orientation case provides slightly better BER enhancements.

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)0.001
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 score0.951
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.073
GPT teacher head0.305
Teacher spread0.231 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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