MétaCan
Menu
Back to cohort
Record W4382449040 · doi:10.23977/jeis.2023.080108

Design of Reconfigurable Power Amplifier Based on Smith Chart Matching

2023· article· en· W4382449040 on OpenAlexvenueno aff
Wang Yuecheng

Bibliographic record

VenueJournal of Electronics and Information Science · 2023
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAmplifierSmith chartComputer scienceElectronic engineeringLinear amplifierPower (physics)Realization (probability)Matching (statistics)Direct-coupled amplifierPower bandwidthElectrical engineeringRF power amplifierEngineeringOperational amplifierTelecommunicationsImpedance matchingBandwidth (computing)

Abstract

fetched live from OpenAlex

In the architecture of the communication network, the signal passes through various devices and finally reaches the destination terminal. This involves relaying and amplifying the signal, and the power amplifier is the core device in this process and the foundation of the communication system. With the continuous development of communication technology, spectrum resources are becoming increasingly tight, and the principle of fixed frequency band allocation has resulted in underutilized spectrum resources. To make full use of spectrum resources, the realization of the reconfigurable capability of power amplifiers will be studied in the future Focus. Then introduced a power amplifier designed by using Smith chart matching of GaN devices. The reconfigurable aspects of the designed power amplifier are studied. The use of PIN diodes enables the design of reconfigurable switches. Simulate the reconfigurable matching network, through the analysis of the simulation results, realize the switch function, and meet the design requirements. Finally, the input and output matching network and the reconfigurable matching network are tuned and optimized. The simulation results show that the power amplifier can switch between 1680MHz, 1935MHz, and 2040MHz frequency bands, the output power can reach 40-43dBm, and the efficiency is 70%-80%.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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 venueJournal of Electronics and Information ScienceSame topicTelecommunications and Broadcasting TechnologiesFrench-language works237,207