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Record W2006332913 · doi:10.5555/1535571.1535582

Cooperative protocols design for wireless ad-hoc networks with multi-hop routing

2008· article· en· W2006332913 on OpenAlexaff
Yuxia Lin, Joo-Han Song, Vincent W. S. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer networkComputer scienceCooperative diversityWireless ad hoc networkFadingPhysical layerDiversity gainDistributed computingMIMOWireless networkWirelessMulti-user MIMOAd hoc wireless distribution serviceOptimized Link State Routing ProtocolRouting protocolTelecommunicationsRouting (electronic design automation)Channel (broadcasting)

Abstract

fetched live from OpenAlex

Abstract Wireless ad-hoc networks can experience significant performance degradation under fading channels. Spatial diversity has been shown to be an effective way of combating wireless fading with the multipleinput multiple-output (MIMO) technique by transmitting correlated information through multiple antennas. The virtual MIMO technique, which allows multiple wireless stations with single antenna to form a virtual transmission array, is shown to be a viable solution from several recent studies. In this paper, we propose a complete system framework for wireless ad-hoc networks utilizing two different cooperative relaying techniques at the physical layer: the repetition coding and the space-time coding. In the data link layer, two medium access control protocols are proposed to accommodate the corresponding physical layer cooperative diversity schemes. In the network layer, diversity-aware routing protocols are proposed to determine the routing path and the relaying topology. Simulations with both constant bit rate and TCP (transmission control protocol) traffic show significant performance gains of the proposed cooperative relaying schemes.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.127
GPT teacher head0.326
Teacher spread0.199 · 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 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

Citations4
Published2008
Admission routes1
Has abstractyes

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