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Record W2176914859 · doi:10.1002/wcm.2643

A multi‐mode IFFT/FFT processor for IEEE 802.11ac: design and implementation

2015· article· en· W2176914859 on OpenAlexaff
Abdelmohsen Ali, Walaa Hamouda

Bibliographic record

VenueWireless Communications and Mobile Computing · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceFast Fourier transformClock rateComputer hardwareReconfigurabilityThroughputField-programmable gate arrayEmbedded systemIEEE 802.11b-1999WirelessIEEE 802.11TelecommunicationsAlgorithmChip

Abstract

fetched live from OpenAlex

Abstract In this paper, we present 64/128/256/512‐point inverse fast Fourier transform (IFFT)/FFT processor for single‐user and multi‐user multiple‐input multiple‐output orthogonal frequency‐division multiplexing based IEEE 802.11ac wireless local area network transceiver. The multi‐mode processor is developed by an eight‐parallel mixed‐radix architecture to efficiently produce full reconfigurability for all multi‐user combinations. The proposed design not only supports the operation of IFFT/FFT for 1–8 different data streams operated by different users in case of downlink transmission, but also, it provides different throughput rates to meet IEEE 802.11ac requirements at the minimum possible clock frequency. Moreover, less power is needed in our design compared with traditional software approach. The design is carefully optimized to operate by the minimum wordlengths that fulfill the performance and complexity specifications. The processor is designed and implemented on Xilinx Vertix‐5 field programmable gate array technology. Although the maximum clock frequency is 377.84 MHz, the processor is clocked by the operating sampling rate to further reduce the power consumption. At the operation clock rate of 160 MHz, our proposed processor can calculate 512‐point FFT with up to eight independent data sequences within 3.2~μ s meeting IEEE 802.11ac standard requirements. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.000
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 score0.884
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.065
GPT teacher head0.332
Teacher spread0.267 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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