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Record W2005760434 · doi:10.1109/icwmc.2007.95

WLAN Mesh Network Interference Management Using FSO Link Deployment

2007· article· en· W2005760434 on OpenAlexaff
Valavan Rajakumar, Mohammed N. Smadi, Sasthi C. Ghosh, T.D. Todd, Steve Hranilovic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceInterference (communication)Channel allocation schemesFrequency assignmentHeuristicWireless mesh networkChannel (broadcasting)Frequency allocationComputer networkLink (geometry)Wireless networkWirelessAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Frequency channels are assigned in wireless LAN mesh networks subject to strict co-channel interference constraints. Since Wi-Fi may be freely used by other networks, added interference may eventually invalidate the original frequency assignment, making full link activation impossible. In this paper we address this problem by selectively installing supplementary free space optical (FSO) links when RF link performance has deteriorated. We first formulate the channel assignment problem with the objective of maximizing the number of simultaneous link activations while satisfying cumulative RF interference constraints. A proof is given for the NF-completeness of the joint frequency assignment and FSO link placement problem. We then propose an efficient heuristic to solve the channel assignment problem using a genetic algorithm. Our comparisons show that the proposed algorithm gives good results compared with computed bounds.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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
Published2007
Admission routes1
Has abstractyes

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