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Record W2122198105 · doi:10.1109/icc.2006.254726

Traffic Engineering in BFWA Mesh Networks at Millimeter Wave Band

2006· article· en· W2122198105 on OpenAlexaff
Junaid Ali Khan, Hussein Alnuweiri

Bibliographic record

Venue2006 IEEE International Conference on Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceWireless mesh networkScalabilityMesh networkingComputer networkExtremely high frequencyDistributed computingChannel (broadcasting)Interference (communication)Channel allocation schemesRadio spectrumControl reconfigurationElectronic engineeringWirelessWireless networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Inherent difficulties in millimeter-wave radio operations, such as higher atmospheric attenuation, especially during rainy times, motivated the use of mesh architecture in millimeter-wave band for broadband fixed wireless access (BFWA) networks. When used with highly directional antennas, these mesh networks also provide better frequency reuse. A recent proposed architecture for such networks shows how a link can have multiple radio channels. This paper exploits this property to present a solution that uses distributed dynamic channel allocation (DDCA) to reconfigure the link capacities to achieve better Traffic Engineering. DDCA works by adding or removing channels from a link while satisfying interference constraints, based on current network conditions. The paper proposes a DDCA algorithm and then integrates it with routing. The distributed dynamic nature of the algorithm provides true scalability with fast and dynamic reconfiguration of the network. Simulation results show that the proposed solution provides better performance than solutions that employ a fixed channel allocation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.895

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 head0.063
GPT teacher head0.266
Teacher spread0.203 · 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
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
Published2006
Admission routes1
Has abstractyes

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