MétaCan
Menu
Back to cohort

4.1 A 39GHz-Band CMOS 16-Channel Phased-Array Transceiver IC with a Companion Dual-Stream IF Transceiver IC for 5G NR Base-Station Applications

2020· article· en· W3016052205 on OpenAlexaboutno aff
H.-C. Park, Dong‐Woo Kang, S. M. Lee, B. Park, Kyu Hong Kim, Juyul Lee, Yuuichi Aoki, Young Yoon, S. Lee, Dae‐Young Lee, Daehyun Kwon, S. Kim, Jongyun Kim, W. Lee, C. Kim, S. Park, Jongsun Park, Bohee Suh, Jaehyuk Jang, M. Kim, Donggyu Minn, I. Park, Sung‐Soo Kim, K. Min, Seung Ho Jeon, An-Sang Ryu, Y. Cho, Seung Tae Choi, Kyu Hwan An, Y. Kim, J. H. Lee, Jaeman Son, Sung-Gi Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsnot available
Fundersnot available
KeywordsChipsetTransceiverCMOSBiCMOSPhased arrayElectrical engineeringElectronic engineeringEngineeringComputer scienceAntenna (radio)ChipTransistor

Abstract

fetched live from OpenAlex

Increasing demands on high-data-rate and low-latency cellular communications are accelerating the developments of millimeter-wave (mm-wave) systems for 5G NR in 28 and 39GHz bands. In order to provide the 5G communication systems worldwide, high-performance and low-cost RF chipset solutions are required. Recently, 5G mm-wave CMOS/BiCMOS RF phased-array transceivers for the 28GHz band have been reported [1]–[5]. However, there are very limited reports for the 39GHz band [6], which is one of the main frequency bands in the US, Canada, China and other countries. In this paper, we present both a 39GHz 16-channel RF phased-array transceiver IC in 28nm CMOS and a dual-stream IF transceiver IC in 65nm CMOS. These chipsets can be scaled up to >500 RF phased-array elements and support dual-stream (MIMO) in 5G NR base-station applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.006

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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations128
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicRadio Frequency Integrated Circuit DesignFrench-language works237,207