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Record W2102044567 · doi:10.1109/glocom.2010.5684193

Throughput Improvement in Multi-Radio Multi-Channel 802.11a-Based Wireless Mesh Networks

2010· article· en· W2102044567 on OpenAlex
Aizaz U. Chaudhry, Roshdy H. M. Hafez, Osama Aboul‐Magd, S. Mahmoud

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 institutionsCarleton University
Fundersnot available
KeywordsWireless mesh networkComputer networkThroughputMesh networkingComputer scienceSwitched meshOrder One Network ProtocolShared meshChannel (broadcasting)Network topologyInterference (communication)Wireless networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

Single-radio mesh routers operating on a single channel suffer from low throughput due to collisions. Equipping mesh routers with multiple radios operating on non-overlapping channels can significantly improve the throughput. However, the assignment of channels to radios in a multi-radio mesh network is a challenging task. In this paper, we propose a channel assignment algorithm, TICA (Topology-controlled Interference-aware Channel-assignment Algorithm), which significantly improves network throughput by minimizing interference within the mesh network using a novel approach of controlling the network topology based on power control before intelligently assigning the channels to the multi-radio mesh routers, as well as guaranteeing network connectivity.

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)0.001
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 score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.252
Teacher spread0.233 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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