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Practical Aspects of Interference Management in Wireless Networks Deployment Experiences at CRC

2007· article· en· W2064142631 on OpenAlexaff
A.L. Brandão, Shaun Luong, Mustapha Bennai, John Sydor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsComputer scienceComputer networkWirelessWireless networkWireless broadbandRadio resource managementTelecommunicationsMultipath propagationWiMAXNetwork packetChannel (broadcasting)

Abstract

fetched live from OpenAlex

Delivering broadband to the user end via wireless has been considered a secondary option and exercised only when the wired link solution computes an unsatisfactory investment return. This paradigm is changing, however, with a number of WiFi metropolitan networks promising profitability in niches traditionally operating with cables. It is expected that these new wireless enterprises will face innumerous obstacles of practical nature, as they grow and deploy in large scale. Problems with large scale wireless deployments may range from coexistence and interference amongst different service providers to billing procedures. In such a scenario, the Communications Research Centre (CRC) set out to design, develop, and deploy a wireless broadband system. Besides the challenges of designing a whole system, CRC engineers were faced with important RF management issues and some of which are the subject of this paper, namely: RF management related to TCP/IP packet duplication over the air due to multipath propagation, intra-sector communication within a multi sectored cell system and the downlink near-far power problem, to name a few. This work highlights critical problems, that we found unique and not reported before, that are most likely to be encountered by emerging networks (WiMax and WiFi) and proposes solutions to overcome them.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.461

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 communication0.0000.000
Open science0.0020.002
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 head0.052
GPT teacher head0.352
Teacher spread0.301 · 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
Published2007
Admission routes1
Has abstractyes

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