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Record W3015107633 · doi:10.1109/mmm.2020.2971183

Doherty PAs for 5G Massive MIMO: Energy-Efficient Integrated DPA MMICs for Sub-6-GHz and mm-Wave 5G Massive MIMO Systems

2020· article· en· W3015107633 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 Microwave Magazine · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMIMO3G MIMOMulti-user MIMOComputer scienceReliability (semiconductor)Electronic engineeringWirelessInterference (communication)Radio frequencyComputer networkTelecommunicationsEngineeringChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

To accommodate growing user demand for faster data rates and extensive connectivity, modern wireless communication systems must evolve to support a sharply increasing number of subscribers, all requesting service at the same time. This trend encourages the broad application of multiple input/multiple output (MIMO) systems. In fact, MIMO techniques can increase data rates, coverage of service areas, and communication reliability without additional RFs. In recent proposals for 5G systems, the required separate RF chains in massive MIMO RF front ends can reach up to 256, with bandwidths of up to 800 MHz per RF chain [1], [2]. Massive MIMO is a critical technology that helps significantly in increasing network capacity and spectral efficiency, while reducing wireless network interference, ultimately improving the end-user experience.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.213
Teacher spread0.192 · 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