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Record W2072872070 · doi:10.1109/ainaw.2007.261

Multi-channel Busy-tone Multiple Access for Scalable Wireless Mesh Network

2007· article· en· W2072872070 on OpenAlexaff
Hairong Zhou, Chi‐Hsiang Yeh, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsComputer networkComputer scienceWireless mesh networkNetwork packetChannel (broadcasting)ThroughputMultiple Access with Collision Avoidance for WirelessAccess controlHidden node problemScalabilityMedia access controlControl channelWirelessMesh networkingWireless networkRouting protocolWi-Fi arrayBase stationTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes a multi-channel medium access control (MAC) protocol for wireless mesh networks (WMNs) by using busy tones to prevent data packet collisions at data channels. Multi-channel MAC schemes can achieve higher network throughput than single channel MAC schemes in multihop wireless networks. It is especially appealing to exploit multiple channels in WMNs which has high capacity requirement to support backbone multimedia applications. Most previously proposed MAC protocols make use of the RTS/CTS mechanism to deal with data packet collisions caused by exposed/hidden terminal problems in multihop environment. However, when multiple channels are used for data transmissions, the RTS/CTS mechanism can no longer handle the exposed/hidden terminal successfully. By investigating the special features of WMN architecture, we apply the busy tone solution into the medium access control mechanism for WMNs, in which mesh nodes have no limit on power consumption. In this paper, we clearly presented the idea and operation of our proposed multichannel MAC protocol for WMNs. Comprehensive simulation is underway to compare the performance of our proposed mechanism with that of other RTS/CTS-based multi-channel MAC protocols.

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 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 score0.763
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 integrity0.0000.000
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 head0.033
GPT teacher head0.301
Teacher spread0.267 · 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.

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

Citations3
Published2007
Admission routes1
Has abstractyes

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