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Record W2116082005 · doi:10.5555/1898953.1899158

Multi-clock pipelined design of an IEEE 802.11a physical layer transmitter

2006· article· en· W2116082005 on OpenAlexaff
Maryam Mizani, Daler Rakhmatov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPhysical layerComputer scienceField-programmable gate arrayHandshakingTransmitterVirtexEmbedded systemThroughputLatency (audio)Pipeline (software)WirelessComputer hardwareChannel (broadcasting)Computer networkTelecommunications

Abstract

fetched live from OpenAlex

Among different wireless LAN technologies 802.11a has recently become popular due to its high throughput, large system capacity, and relatively long range. In this paper, we propose a reconfigurable architecture for the 802.11a physical layer transmitter, which has low la-tency and low power consumption due to its pipelined structure. Data from the MAC layer can continuously flow through the pipeline without excessive buffering and handshaking within the physical layer. Dynami-cally reconfiguring this architecture to work at any data rate supported by 802.11a (eight different modes) can be performed within a few cycles, simply by adjusting the period of two clock signals and changing the value of a 3-bit control signal. Our architecture, prototyped on a Xilinx Virtex-II Pro FPGA, occupies the area of 2059 slices and is estimated to consume 500 mW. These figures can be improved substantially in custom ASIC implementations. 1.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.235
Teacher spread0.208 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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