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Record W4405968108 · doi:10.1109/lwc.2024.3524722

Phase Noise Resilient Neural Transceivers for High Data-Rate Sub-THz Links

2025· article· en· W4405968108 on OpenAlexaff
Dileepa Marasinghe, Le Hang Nguyen, Nandana Rajatheva, Matti Latva‐aho

Bibliographic record

VenueIEEE Wireless Communications Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsTransceiverComputer sciencePhase noiseNoise (video)TelecommunicationsElectronic engineeringWirelessArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Phase noise (PN) poses a significant challenge in sub-terahertz (sub-THz) communications, alongside the necessity for low peak-to-average power ratio (PAPR) transmissions. This letter introduces an end-to-end learned single-carrier (SC) neural transceiver, which consists of a PN-resilient and PAPR-constrained transmitter utilizing a trainable pilot scheme combined with a deep neural receiver tailored for sub-THz. The learned transceiver effectively compensates for both correlated and uncorrelated PN and the flat-fading line-of-sight (LOS) channel while maintaining lower PAPR. The results show a substantial reduction in pilot overhead while delivering superior spectral efficiency and up to 1.2 dB PAPR gains over the conventional baselines.

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 score0.148
Threshold uncertainty score0.803

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.040
GPT teacher head0.317
Teacher spread0.277 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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