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Record W2016444633 · doi:10.1049/iet-com:20060224

Cross-layer distributed approach for optimal rate allocation for homogeneous wireless multicast

2007· article· en· W2016444633 on OpenAlexaff
Amr Mohamed, Hussein Alnuweiri

Bibliographic record

VenueIET Communications · 2007
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMulticastComputer scienceComputer networkSource-specific multicastDistributed computingXcastPragmatic General MulticastWireless networkProtocol Independent MulticastWireless ad hoc networkAsynchronous communicationDistributed algorithmHeterogeneous networkWireless

Abstract

fetched live from OpenAlex

Multicast-based data communication is an efficient communication scheme especially in multihop ad hoc networks where the MAC layer is based on one-hop broadcast from one source to multiple receivers. The problem of resource allocation for a set of homogeneous multicast sessions over multihop wireless network is addressed. An iterative algorithm is proposed that achieves the optimal rates for a set of multicast sessions such that the aggregate utility for all sessions is maximised. The authors demonstrate analytically and through simulations that the algorithm achieves optimal resource utilisation while guaranteeing fairness among multicast sessions. The algorithm in network environments with asynchronous distributed computations has been further analysed. Two implementations for the algorithm based on different network settings are presented and show that the algorithm not only converges to the optimal rates in all network settings but it also tracks network changing conditions, including mobility and dynamic channel capacity.

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 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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.048
GPT teacher head0.330
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2007
Admission routes1
Has abstractyes

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