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Record W3081615953 · doi:10.1109/lwc.2020.3021026

Towards High Data Rates in Dynamic Environments Using Hybrid Cell-Free Massive MIMO/Small-Cell System

2020· article· en· W3081615953 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Wireless Communications Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceTelecommunications linkMIMORician fadingFadingReal-time computingComputer networkAlgorithmDecoding methods

Abstract

fetched live from OpenAlex

We propose a hybrid cell-free (CF) massive multiple-input multiple-output (mMIMO)/small-cell (SC) system that can reap the capabilities of CF mMIMO and SC systems in providing static and moving users with high data rates. A certain association criterion and system operation are proposed. Then, the achievable downlink (DL) rates of moving and static users are derived accordingly assuming Rician fading channels. The performance of the proposed system is compared with that of CF mMIMO and SC systems. Results reveal that, unlike CF mMIMO and SC systems, the proposed hybrid system can provide high DL rates to users with different velocities simultaneously.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score1.000

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 science0.0030.001
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 head0.030
GPT teacher head0.238
Teacher spread0.207 · 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