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OP Analysis of Alamouti/MRC NOMA System with SDR-Based Real-Time Implementation

2024· article· en· W4400277422 on OpenAlexaff
Lütfullah Özkan, Büşra Demirkol, Saliha Büyükçorak, Oğuz Kucur, Güneş Karabulut Kurt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNomaComputer scienceMaximal-ratio combiningTelecommunicationsFadingDecoding methodsTelecommunications link

Abstract

fetched live from OpenAlex

Motivated by the great potential of multi-antenna, multi-user non-orthogonal multiple access (NOMA) systems for 5G+, this paper presents an outage probability (OP) analysis of a down-link multi-user NOMA network with Alamoutilmaximum ratio combining (AlamoutiIMRC) antenna diversity. Herein, a base station using Alamouti coding simultaneously serves mul-tiple users with MRC. We derive the exact and asymptotic OP expressions for users over the Rayleigh fading channel. In addition, we also constitute a software-defined radio-based three-users 2x2 AlamoutiIMRC test-bed to investigate the feasibility of the considered network in a real-time manner. Finally, the analytical expressions are verified through the test-bed results and the Monte Carlo simulations.

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 score0.307
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.230
Teacher spread0.225 · 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
Published2024
Admission routes1
Has abstractyes

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