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Record W2079475160 · doi:10.1145/1185373.1185398

A link performance model for multi-user wireless fading channels

2006· article· en· W2079475160 on OpenAlex
Xinhua Ling, Mehrdad Dianati, J.W. Mark, Xuemin Shen

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 Waterloo
Fundersnot available
KeywordsFadingComputer scienceComputer networkLink (geometry)WirelessChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

The two-state Markov chain has been widely used to model fading channels in the performance study of upper-layer communication protocols in wireless networks. It can be used to model transmission success/failure based on the physical characteristics of the transmission channel. However, for shared wireless links, packet transmission depends on both the status of the link and the scheduling strategy used. In this poster, we propose a novel four-state Markov model, which takes into consideration the impacts of channel fading and scheduling on packet transmission over shared wireless links. It is further abstracted to an effective two-state Markov chain to facilitate analytical performance evaluation. To demonstrate the efficacy of the proposed model, we apply it to study the throughput, delay and delay jitter of a saturated traffic source, and the packet dropping probability at the network layer for data traffic under a buffer overflow dropping policy. Simulation results to demonstrate the reasonableness of the proposed model are also presented. © 2006 ACM.

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: Methods · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.623

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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