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Record W2338827210 · doi:10.1109/tmtt.2016.2550039

Power-Scalable Wideband Linearization of Power Amplifiers

2016· article· en· W2338827210 on OpenAlex

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

VenueIEEE Transactions on Microwave Theory and Techniques · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAdjacent channelAmplifierWidebandLinearizationElectronic engineeringBandwidth (computing)RF power amplifierElectrical engineeringPower bandwidthBroadbandEngineeringAdjacent channel power ratiodBcComputer scienceTelecommunicationsCMOSPhysicsNonlinear system

Abstract

fetched live from OpenAlex

In this paper, a wideband RF power amplifier (PA) linearization technique based on the addition of a linearization amplifier (LA) is proposed. The technique is suitable for low-powered and low-cost small cell and fifth-generation (5G) large-scale multi-antenna PA designs since its power and cost overhead does not increase with the input signal bandwidth and because they are scalable with the power range and cost of the PA. In-depth analysis of the proposed technique is provided and its linearization improvement mechanisms are described. As a proof of concept, a prototype LA was designed and fabricated to linearize a broadband 6-W class-AB PA with a center frequency of 850 MHz. When stimulated by a wideband 40-MHz signal, the PA's adjacent channel leakage ratio (ACLR) was improved by up to 13 dB after the addition of the LA. This enabled the PA to achieve an ACLR of about -45 dBc without the use of any other linearization techniques. Furthermore, significant ACLR improvements were observed for even signals with wider bandwidths up to 160 MHz.

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 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: none
Teacher disagreement score0.973
Threshold uncertainty score0.596

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 integrity0.0000.000
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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