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Record W3148798908 · doi:10.18280/ts.380112

Investigation of Ideal Number User Terminals with Spectrum Efficiency in Next Generation Wireless Communication Systems

2021· article· en· W3148798908 on OpenAlexvenueno aff
Osman Dikmen, Selman Kulaç

Bibliographic record

VenueTraitement du signal · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSpectral efficiencyBase stationMIMOComputer scienceIdeal (ethics)Interference (communication)WirelessMulti-user MIMOReuseElectronic engineeringComputer networkMathematicsTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Suppose that a multi-user multiple-input multiple-output (MIMO) system is developed from scratch to equally envelop a defined region with optimal spectrum efficiency (SE) in next generation wireless communication systems such as sixth-generation (6G) and beyond networks. What are the ideal number of user terminals U, number of base stations antennas, and used pilot reuse factor? The purpose of this paper is to address this specific issue. Three interference levels are specified for this. Based on these interference levels, signal-to-interference-and-noise ratios (SINRs) are extracted. Closed-form spectrum efficiency equations are thus obtained. As a function of the base station (BS) antenna number, simulations are carried out considering multiple pilot reuse factors and diverse processing schemes such as Maximum Ratio Combining (MRC) and Zero-Forcing (ZF). From the results, it is understood that U varies according to the processing schemes. Therefore, evaluating the results considering the fixed number of users K will not give an accurate result in determining the design parameters for the next generation communication systems. In general, these results are useful statements that spectrum efficiency is maximized when the ideal number of users U is used in multi-cell systems.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.571

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)0.0000.000

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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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
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

Citations1
Published2021
Admission routes1
Has abstractyes

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