Pith. Sign in

RECORD

End-to-End Learning of Pulse-Shaper and Receiver Filter in the Presence of Strong Intersymbol Interference

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
S{o}ren F{o}ns Nielsen, Francesco Da Ros, Mikkel N. Schmidt, Darko Zibar

Signal Processing · Submitted 22 May 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower complexity), for 4-PAM 100 GBd IM/DD systems.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2405.13367 (version 1)
Pith ID5O5HMFUM, Pith's permanent record of this paper and its review
CitationDarko Zibar, Francesco Da Ros, Mikkel N. Schmidt, S{\o}ren F{\o}ns Nielsen. "End-to-End Learning of Pulse-Shaper and Receiver Filter in the Presence of Strong Intersymbol Interference." arXiv:2405.13367 (2024). https://pith.science/paper/5O5HMFUM
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 1,666 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.21 · randomized assignment 0.01 · states quantitative result 0.31 · compares with prior results 0.24 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower complexity), for 4-PAM 100 GBd IM/DD systems.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower complexity), for 4-PAM 100 GBd IM/DD systems. result 0.95 0.94 0.92 asserted 1.00

What kind of paper the classifier reports this is: simulation study 0.97 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower complexity), for 4-PAM 100 GBd IM/DD systems.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites

Uses the method or tool of 1 paper
  • We use the Adam optimizer [ref] with the OneCLR learning rate policy [ref] setting the maximum learning rate to 10 times the starting learning rate, $lr_0$.

    Adam: A Method for Stochastic Optimization · Simulation framework · 1.00

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.