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Record W1989377671 · doi:10.1109/ita.2010.5454108

Delay performance of CSMA policies in multihop wireless networks: A new perspective

2010· article· en· W1989377671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer networkComputer scienceCarrier sense multiple access with collision avoidanceNetwork packetWireless networkInterference (communication)ThroughputWirelessChannel (broadcasting)Transmission delayPropagation delayExponential backoffDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we study the delay performance of CSMA policies in wireless networks, where the delay is defined as the average time that a silent wireless link needs to wait until it accesses the channel for packet transmission. It is well-known that CSMA policies can incur an access delay that may be correlated over time and may grow exponentially with the network size. This discourages practical implementation of CSMA policies in even mid-sized networks. In this paper, we provide a new perspective on the delay performance of CSMA policies. We present recently developed results for two important interference models and show how CSMA policies can be used to ensure an access delay that is memoryless over time or that does not grow with the network size. The two interference models that we consider are primary interference and the ¿lattice interference graph¿. Our results suggest that CSMA policies can achieve a delay performance, as well as a delay-throughput trade-off, that makes them viable to be used in practice.

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.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.004
GPT teacher head0.213
Teacher spread0.208 · 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

Quick stats

Citations8
Published2010
Admission routes1
Has abstractyes

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