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Record W2167296097 · doi:10.1109/glocom.2008.ecp.988

Minimizing Interference in WiMax/802.16 Based Mesh Networks with Centralized Scheduling

2008· article· en· W2167296097 on OpenAlexaff
Jad El‐Najjar, Brigitte Jaumard, Chadi Assi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsWiMAXComputer networkWireless mesh networkComputer scienceShared meshOrder One Network ProtocolMesh networkingSwitched meshWireless broadbandService setScheduling (production processes)IEEE 802.11sWireless networkDistributed computingWirelessWi-FiTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

WiMax/802.16 mesh network is an emerging infrastructure that offers a cost-effective deployment for highspeed wireless broadband access to the back haul network. However, as in most wireless multi-hop networks, WiMax/802.16 mesh suffers from interference that decreases considerably the throughput and spatial reuse of the network. Interference in WiMax/802.16 mesh is a result of several phenomena, namely concurrent transmissions in the neighborhood and data collisions (that need to be avoided) at a receiver from transmitting nodes that are outside the range of each other (hidden terminal nodes). In this paper, we study the problem of minimizing iInterference (MI) in WiMax/802.16 mesh centralized scheduling networks by appropriately routing end connections and assigning slots to them. The proposed model includes the effect of hidden terminal nodes as well as the interferences coming from neighboring nodes. Results show that power-aware routing and adequate frame size selection yield better network performance, a consequence of the improved network spatial reuse.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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