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Record W2114604542 · doi:10.1109/iwcmc.2008.108

Minimum Interference Channel Assignment for Multicast in Multi-Radio Wireless Mesh Networks

2008· article· en· W2114604542 on OpenAlex
Hoang Lan Nguyen, Uyen Trang Nguyen

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
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsYork University
Fundersnot available
KeywordsMulticastComputer networkComputer scienceSource-specific multicastWireless mesh networkPragmatic General MulticastXcastProtocol Independent MulticastChannel (broadcasting)Shared meshDistributed computingWireless networkSwitched meshWirelessTelecommunications

Abstract

fetched live from OpenAlex

Multi-radio, multi-channel wireless mesh networking is an emerging wireless technology which enables the use of multiple radios in each wireless mesh router. Each radio is assigned to a particular channel based on a channel assignment algorithm in order to solve some objective function, e.g., maximizing network throughput or minimizing wireless interference. Multicast is a form of communication that delivers information from a source to a set of destinations simultaneously. In this paper, we propose a channel assignment (CA) algorithm for multicast using both orthogonal and partially overlapping channels. The algorithm enables the nodes in a multicast tree to operate with minimum interference. We evaluate the performance of the proposed CA using various multicast group sizes and numbers of available channels, and compare it with that of the multi-channel multicast (MCM) algorithm proposed by Zeng et al. (2007).

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 categoriesMeta-epidemiology (narrow)
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 score0.938
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.044
GPT teacher head0.263
Teacher spread0.219 · 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

Citations31
Published2008
Admission routes1
Has abstractyes

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