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Record W3177595899 · doi:10.1016/j.vehcom.2021.100385

PHY layer enhancements for next generation V2X communication

2021· article· en· W3177595899 on OpenAlexaff
Andy Triwinarko, Iyad Dayoub, Soumaya Cherkaoui

Bibliographic record

VenueVehicular Communications · 2021
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversité de Sherbrooke
FundersDirektorat Jenderal Pendidikan TinggiLembaga Pengelola Dana Pendidikan
KeywordsPHYComputer scienceDedicated short-range communicationsPhysical layerComputer networkThroughputWiMAXNetwork packetSpace–time block codeBackward compatibilityLinear network codingMIMOIEEE 802WirelessChannel (broadcasting)TelecommunicationsQuality of service

Abstract

fetched live from OpenAlex

IEEE 802.11p is a robust and mature technology for dedicated short-range communication (DSRC) where several field trials have been carried out, and the performance of various vehicle-to-everything (V2X) communication scenarios has been investigated. On the other hand, other IEEE 802.11 or wireless local area network (WLAN) standards have evolved and offered some techniques to improve the 802.11p standard. The new task group IEEE 802.11bd (TGbd) was formed to explore the future roadmap for V2X and is working toward a new standard called next-generation V2X (NGV). This article investigates the performance of physical (PHY) layer amendments to 802.11p, i.e., the use of low-density parity-check (LDPC) and midambles, multi-input multi-output-space time block coding (MIMO-STBC), dual-carrier modulation (DCM), and extended-range mode. We build and simulate our system in several V2V channel environments, using the packet error rate (PER) and throughput as the performance metrics. Our investigations show a significant PER performance improvement of all techniques compared to the legacy 802.11p standard. In terms of throughput, the new PHY layer enhancements also give a better performance, except for the DCM technique that improves the reliability of the V2V communication in low SNR conditions at the expense of reducing the channel capacity in half.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.084
GPT teacher head0.282
Teacher spread0.198 · 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 designNot applicable
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

Citations32
Published2021
Admission routes1
Has abstractno

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