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Record W2135579584 · doi:10.1109/glocom.2003.1258209

Combining space-time coding with power-efficient medium access control for mobile ad-hoc networks

2004· article· en· W2135579584 on OpenAlexaff
Ramy Farha, Raviraj Adve

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkMobile ad hoc networkWireless ad hoc networkNetwork packetThroughputQuality of servicePower controlAccess controlMedia access controlCoding (social sciences)Linear network codingVehicular ad hoc networkMultiple Access with Collision Avoidance for WirelessAd hoc wireless distribution serviceOptimized Link State Routing ProtocolWirelessPower (physics)Routing protocolTelecommunications

Abstract

fetched live from OpenAlex

This paper investigates the impact of space-time coding on an IEEE 802.11a based power-efficient medium access control (MAC) protocol in a mobile ad hoc network (MANET). The new MAC protocol, MAC-2, preserves energy by sending information at the minimum power needed to reach the destination with a specified packet error rate (PER). The analysis of space-time coding is conducted in practical environments, including spatial correlations between the fades connecting the transmitter and receiver antenna elements. Combining improvements in the physical layer with the new MAC-2 protocol leads to tremendous savings in power and increase in overall network throughput, while maintaining a basic quality of service.

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)0.004
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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