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Record W4394828369 · doi:10.1109/comst.2024.3388511

Evolution of RAN Architectures Toward 6G: Motivation, Development, and Enabling Technologies

2024· article· en· W4394828369 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 Communications Surveys & Tutorials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaInnovation and Entrepreneurship Talent Project of LanzhouNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsRanComputer scienceCognitive sciencePsychologyComputer network

Abstract

fetched live from OpenAlex

In this survey paper, we first provide insights on the evolution of radio access networks (RANs) through investigating the existing paradigms and future trends towards 6G. We then present the fully-decoupled RAN (FD-RAN), which aligns with the trends by integrating existing paradigms and introducing new features such as physical decoupling of uplink and downlink base stations. We also discuss the key technologies enabled by different architectures for further performance improvement and some open issues. We hope that this survey can stimulate more in-depth research on transforming 6G RAN so as to unleash the power of state-of-the-art technologies and meet higher performance requirements in the future.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.722
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.051
GPT teacher head0.283
Teacher spread0.233 · 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