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Record W1963850605 · doi:10.1049/iet-com:20060639

Channel estimation for 5.9 GHz dedicated short-range communications receiver in wireless access vehicular environments

2007· article· en· W1963850605 on OpenAlexaff
H. Abdulhamid, Esam Abdel‐Raheem, Kemal Tepe

Bibliographic record

VenueIET Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceWirelessRange (aeronautics)Channel (broadcasting)TelecommunicationsComputer networkEngineering

Abstract

fetched live from OpenAlex

A channel estimation scheme for the design of a 5.9 GHz dedicated short-range communications receiver for the purpose of vehicle-to-vehicle and vehicle-to-roadside communications is proposed. The receiver is based on the popular wireless local area network standard IEEE 802.11a, but with extended symbol duration. On the basis of analyses and simulations, the proposed receiver has superior design characteristics for the harsh channel conditions of wireless access vehicular environments. The performance of the proposed scheme is tested under varying velocity, signal-to-noise ratios, transmission modes, packet lengths and channel properties. The proposed design improves the overall packet error rate performance, enabling the system to achieve higher data rates and larger packet lengths at high relative velocities. Simulation results show slight improvements in the quadrature phase-shift keying transmission, whereas there is substantial improvement in the cases of 16- and 64-ary quadrature amplitude modulations compared to conventional design.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.340
Teacher spread0.285 · 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 designBench or experimental
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

Citations18
Published2007
Admission routes1
Has abstractyes

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