MétaCan
Menu
Back to cohort
Record W4393144445 · doi:10.1049/icp.2023.2271

Enhancing achievable information rate via RMS-optimized digital pre-emphasis for coherent short reach applications

2023· article· en· W4393144445 on OpenAlexaff
Waichiu Ng, Qing-Yi Guo, J. Chang, Chin Pan, Meng Qiu, Xifan Tang, Zhiping Jiang, Can Li

Bibliographic record

VenueIET conference proceedings. · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsEmphasis (telecommunications)Bandwidth (computing)Computer scienceElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

We revisit the achievable information rate (AIR) due to the coupling between digital pre-emphasis and the bandwidth-limited channel under Tx constraints. We propose and experimentally verify, for the first time, a parameter-insensitive, RMS-optimized pre-emphasis filter for shaping high-frequency signal components to enhance the operational AIR.

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 score0.901
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIET conference proceedings.Same topicOptical Network TechnologiesFrench-language works237,207