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Record W4312649270 · doi:10.1109/tvt.2022.3218048

The Effect of Hardware Impairments on the Error Bounds of Localization and Maximum Likelihood Estimation of mm-Wave MISO-OFDM Systems

2022· article· en· W4312649270 on OpenAlexaff
Deeb Assad Tubail, Busra Ceniklioglu, Ayşe Elif Canbilen, İbrahim Develı, Salama Ikki

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsLakehead University
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsEstimatorCramér–Rao boundTelecommunications linkOrthogonal frequency-division multiplexingAlgorithmTransmitterEstimation theoryChannel (broadcasting)Base stationFisher informationProcess (computing)Maximum likelihoodMathematicsComputer scienceStatisticsTelecommunications

Abstract

fetched live from OpenAlex

This work investigates the localization process in milimeter-wave ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mm$</tex-math></inline-formula> -Wave) multiple-input single-output (MISO) OFDM systems considering hardware impairments (HWIs) at both the base station (BS) and the mobile station (MS). The localization is performed on MS board by estimating the downlink channel parameters using a maximum likelihood (ML) estimator, and then transforming them to the localization parameters. Besides, the Fisher information matrix (FIM) is utilized to assess the accuracy of the estimation processes. The limits of the localization is calculated in terms of the position error bound (PEB). The obtained results of the computer simulations show the destructive impacts of the HWIs on the localization process with respect to the effective SNR, and the number of pilot transmissions, transmitter antennas and subcarriers.

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: none
Teacher disagreement score0.521
Threshold uncertainty score0.477

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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