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Record W2231385767

Physical Layer Measurements for an 802.11 Wireless Mesh Network Testbed

2011· article· en· W2231385767 on OpenAlexaff
Stanley Ng, Ted H. Szymanski

Bibliographic record

VenueInternational Conference on the Digital Society · 2011
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer networkWireless mesh networkComputer scienceMesh networkingOrder One Network ProtocolSwitched meshShared meshTime division multiple accessPhysical layerWireless networkTestbedService setWirelessWi-FiTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Physical layer measurements for an infrastructure 802.11 Multichannel MultiBand Wireless Mesh Network testbed are described. Each wireless router node design consists of a Linux processor with multiple 802.11b/g transceivers operating in the 5 GHz band for backhauling, and multiple 802.11 transceivers in the 2.4 GHz band for end-user service. Each transceiver consists of a MAC and base-band processor (BBP) in addition to a radio. A Linux-based device driver has been modified to adjust the physical layer parameters. The 802.11 standard specifies three orthogonal channels, 1, 6, and 11. The routers can be programmed to implement any static mesh binary tree topology by assigning orthogonal frequency-division multiplexing (OFDM) channels to network edges. The routers can be programmed to implement any general mesh communication topology by using a time division multiple access (TDMA) frame schedule, and assigning OFDM channels to network edges within each TDMA frame. Preliminary measurements of co-channel interference and the signal to interference and noise (SINR) ratio for the network testbed are presented, using omni-directional antenna and the 802.11b operation mode. This data can be used to optimize the performance of large infrastructure Wireless Mesh networks using 802.11 technology. Index Terms—wireless mesh network; 802.11; co-channel in- terference; noise; SINR;

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.596

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 communication0.0010.001
Open science0.0020.000
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 head0.143
GPT teacher head0.300
Teacher spread0.157 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations1
Published2011
Admission routes1
Has abstractyes

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