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Record W2145214975 · doi:10.1109/ccece.2005.1557295

Scalability and performance analysis of IEEE 802.11a

2006· article· en· W2145214975 on OpenAlexaff
Abu Nasser Mohammed Abdullah, H. Moinudeen, Wajdi Fawzi Mohammed Al-Khateeb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsConcordia University
Fundersnot available
KeywordsIEEE 802.11a-1999Orthogonal frequency-division multiplexingComputer scienceComputer networkIEEE 802WirelessScalabilityIEEE 802.11g-2003Electronic engineeringIEEE 802.11Wireless lanTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Wireless LAN (WLAN) has become the most talked about technology in last decade. Using radio frequency (RF) technology, WLANs transmit and receive data over the air, through walls, ceilings and even cement structures, without wired cabling. The IEEE 802.11 specification is a wireless LAN standard developed by the IEEE in order to specify an "over the air" interface between a wireless client and a base station or access point, as well as among wireless clients. The legacy standard specifies a 2.4 GHz operating frequency using frequency hopping or direct sequence spread spectrum modulation with initial data rates of 1 or 2 Mbps. But, later revisions have used modulation techniques like orthogonal frequency division multiplexing (OFDM) and complementary code keying (CCK) and increased the data rate tremendously. Recently, IEEE 802.11g is providing data rates up to 54 Mbps. This paper presents the performance and scalability analysis of IEEE 802.11a standard in terms of several chosen parameters

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)0.003
metaresearch head score (Gemma)0.014
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.226
Teacher spread0.217 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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