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Record W2624181897

D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies

2015· article· en· W2624181897 on OpenAlexaboutno aff
D. Aziz, Paolo Baracca, E. de Carvalho, Roberto Fantini, Nandana Rajatheva, Petar Popovski, Jesper H. Sørensen, Henning Thomsen, Tommy Svensson, J. Li, Tilak Rajesh Lakshmana, Yun Sui, Anass Benjebbour, Satoshi Suyama, Richard Abrahamsson, Gábor Fodor, Aymen Jaziri, Dinh Thuy Phan Huy, Hajer Khanfir, Raphaël Visoz, Yichao Yuan, Yohan Lejosne, Abdulaziz Mohamad, Ahmed Saadani, Soulayma Smirani, Martin Kurras, Lars Thiele, Mario Castañeda, Nikola Vučić, Marcin Iwanow, S. Bouazabia, Hadi Ghauch, Mikael Skoglund, S. M. Kim, Mats Bengtsson, Rami Mochaourab, Tero Ihalainen, Wolfgang Zirwas, Paweł Sroka, K. Ratajczak, Krzysztof Bąkowski, Kun Guo, Gian Michele Dell’Aera, Bruno Melis, Marco Caretti, Carsten Bockelmann, F. Lenkeit, Antti Tölli, Keeth Jayasinghe, Sandra Roger Varea, D. Soler, José Francisco Monserrat del Río, Mikael Sternad

Bibliographic record

VenueRiuNet (Universitat Politècnica de València) · 2015
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsnot available
Fundersnot available
KeywordsNode (physics)Computer scienceTransmission (telecommunications)TelecommunicationsAntenna (radio)Computer networkEngineering
DOInot available

Abstract

fetched live from OpenAlex

This document provides the most recent updates on the technical contributions and research challenges focused in WP3. Each Technology Component (TeC) has been evaluated under possible uniform assessment framework of WP3 which is based on the simulation guidelines of WP6. The performance assessment is supported by the simulation results which are in their mature and stable state. An update on the Most Promising Technology Approaches (MPTAs) and their associated TeCs is the main focus of this document. Based on the input of all the TeCs in WP3, a consolidated view of WP3 on the role of multinode/multi-antenna transmission technologies in 5G systems has also been provided. This consolidated view is further supported in this document by the presentation of the impact of MPTAs on METIS scenarios and the addressed METIS goals.

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)0.003
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.074
GPT teacher head0.254
Teacher spread0.180 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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