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Record W4293868724 · doi:10.1109/ims37962.2022.9865388

Hardware-Efficient Implementation of Piece-wise Digital Predistorters for Wideband 5G Transmitters

2022· article· en· W4293868724 on OpenAlex
Mohammed Almoneer, Hoda Barkhordar-Pour, Patrick Mitran, Slim Boumaiza

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 IEEE/MTT-S International Microwave Symposium - IMS 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer sciencePredistortionWidebandRange (aeronautics)Computer hardwareFunction (biology)ChipSquare rootRouting (electronic design automation)Computer engineeringEmbedded systemElectronic engineeringTelecommunicationsBandwidth (computing)Mathematics

Abstract

fetched live from OpenAlex

This paper proposes a hardware-efficient implementation of the digital predistortion (DPD) engine in wideband fifth-generation (5G) transmitters. This efficient implementation employs a DPD model comprising a piece-wise linear (PWL) function that covers unequal non-overlapping segments of the squared magnitude of the input signal range. When compared with prior works, the proposed PWL-based model is less complex in that it does not require the implementation of the square-root function. Furthermore, a parallelized implementation of the PWL-based DPD engine is proposed to reduce the required hardware processing rate. Compared to previous works, the proposed implementation simplifies the on-chip routing structure needed.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.528
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.238
Teacher spread0.231 · 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