MétaCan
Menu
Back to cohort
Record W2035289586 · doi:10.1109/cjece.2014.2317756

A High-Throughput VLSI Architecture for Real-Time Optical OFDM Systems With an Efficient Phase Equalizer

2014· article· en· W2035289586 on OpenAlexvenueno aff
Reza Ghanaatian, Mahdi Shabany, Morteza H. Shoreh

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsThroughputComputer scienceOrthogonal frequency-division multiplexingTransmitterMultiplexingCMOSVery-large-scale integrationFast Fourier transformElectronic engineeringEqualization (audio)Channel (broadcasting)Computer hardwareEmbedded systemAlgorithmTelecommunicationsWirelessEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel high-throughput very large scale integrated circuit architecture for a real-time implementation of intensity modulation direct detection optical orthogonal frequency division multiplexing system is proposed, achieving the highest throughput reported to date. The proposed architecture utilizes a fast, pipelined, and parallel inverse fast Fourier transform/fast Fourier transform in the transmitter/receiver, which is customized to satisfy the throughput requirements of the advanced optical systems. In addition, an efficient high-accuracy equalization method is developed, improving the system performance compared with the conventional linear equalizers. To evaluate the system performance, the OptiSystem software is used to model the optical channel and a Virtex-6 ML-605 evaluation board is used as the implementation platform. Moreover, the synthesis results in a 180-nm CMOS technology prove that the proposed architecture achieves a sustained throughput of 22.5 Gb/s with a 4.89-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> core area.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.539
Threshold uncertainty score0.678

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 head0.004
GPT teacher head0.180
Teacher spread0.176 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Electrical and Computer EngineeringSame topicOptical Network TechnologiesFrench-language works237,207