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Record W3134825452 · doi:10.1109/jphot.2021.3064997

Experimental Demonstrations of Matching Filter-Free Digital Filter Multiplexed SSB OFDM IMDD Transmission Systems

2021· article· en· W3134825452 on OpenAlexaff
Zhuqiang Zhong, Wei Jin, Yixian Dong, Abdulai Sankoh, Jiaxiang He, Yanhua Hong, R. P. Giddings, I. Pierce, Maurice OaSullivan, Jeffrey Lee, G. Mariani, Tim Durrant, Jianming Tang

Bibliographic record

VenueIEEE photonics journal · 2021
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsCiena (Canada)
FundersEuropean Regional Development FundLlywodraeth Cymru
KeywordsComputer scienceElectronic engineeringOrthogonal frequency-division multiplexingTransmission (telecommunications)Filter (signal processing)Digital signal processingSidebandMultiplexingModulation (music)Design for manufacturabilityDigital filterCompatible sideband transmissionChannel (broadcasting)TelecommunicationsPhysicsComputer hardwareEngineeringElectrical engineeringRadio frequencyAcoustics

Abstract

fetched live from OpenAlex

Matching filter (MF)-free digital filter multiplexed (DFM) single sideband (SSB) OFDM intensity modulation and direct detection (IMDD) dual-channel transmissions of 51.25Gbit/s over 25km SSMFs are experimentally demonstrated. It is shown that both transmission system impairments and digital filter characteristic variations can only lead to <1dB transmission performance degradations. Compared with the MF-free DFM-based double sideband (DSB) OFDM technique, the SSB technique has a similar receiver DSP complexity and provides almost twice the maximum signal transmission capacity. When compared with a conventional DFM technique incorporating a dedicated shaping and matching filter pair for each channel, the present SSB technique achieves a 10-fold reduction of receiver DSP complexity and 7.82Gbit/s of additional signal transmission throughput. We demonstrate these throughput and complexity advantages by modelling and measurement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.793

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 integrity0.0000.001
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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 designBench or experimental
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

Citations7
Published2021
Admission routes1
Has abstractyes

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