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Record W2125880075 · doi:10.1109/lcn.2008.4664292

CDMA/MPLS platforms for WIMAX mesh networks

2008· article· en· W2125880075 on OpenAlexaff
Imam Al-wazedi, A.K. Elhakeem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer networkTime division multiple accessComputer scienceWiMAXCode division multiple accessTransmitterMulti-frequency time division multiple accessFrequency-division multiple accessNetwork packetWirelessTelecommunicationsMIMOMIMO-OFDMOrthogonal frequency-division multiplexing

Abstract

fetched live from OpenAlex

Wide band mesh or star oriented networks have recently become a subject of greater interest. Providing wideband access for a variety of applications has led to the inception of WIMAX. Classic access techniques such as FDMA and TDMA have been norm for such networks. Their maximum transmitter power is much less than their TDMA and FDMA counter parts, which are very important assets for mobile operation. In this paper we introduce a code division multiple access/Time division duplex technique CDMA/TDD for such networks. The CDMA approach is an almost play and plug technology for wireless access, making it amenable for implementation by WIMAX service station. In addition we adopt a virtual circuit (label) to improve packet overhead efficiency. We outline briefly the new transmitter and receiver structures then evaluate efficiency, delay and delay jitter. By analysis we show the advantages over classic counter parts with respect to total network efficiency achievable especially for larger number of hops.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.006

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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2008
Admission routes1
Has abstractyes

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