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Record W2915654001 · doi:10.1109/nana.2018.8648729

Low-complexity Optimal Scheduler for LTE Over LAN Cable

2018· article· en· W2915654001 on OpenAlexaff
Syed Hassan Raza Naqvi, Shahida Jabeen, P.-H. Ho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRadio access networkBasebandComputer scienceComputer networkBase stationLeverage (statistics)Radio over fiberRemote radio headBandwidth (computing)Optical fiberTelecommunicationsMobile station

Abstract

fetched live from OpenAlex

Centralized Radio Access Network (C-RAN) is a promising architecture for handling complex interference scenarios generated by massive antennas that are anticipated in the next generation (5G) mobile networks. In conventional C-RANs, the fronthaul link between a Base Band Unit (BBU) and a Remote Radio Unit (RRU) deploys digital baseband signaling using an optical fiber link. Recently, copper-based analog fronthauls have been proposed as a low cost alternative to the fiber optics based fronthauls. These cable based fronthauls for a RAN, also known as Radio over Cable (RoC), leverage the existing LAN cable architecture to meet the high bandwidth requirements with almost negligible cost. In this paper, we propose an analog scheduler for mapping radio users over a LAN cable with multiple twisted pairs. More specifically, we discuss an LTE MISO based RAN and propose a user scheduler for allocating resources on the LAN cable. Through extensive numerical simulations, we show that a low complexity problem can be formulated to obtain quasi-optimal user schedules for LoC.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.595

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.260
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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