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Image Transmission Using SC-FDMA System Over mmWave Measured Channel at 29.5 GHz

2019· article· en· W2945569266 on OpenAlexaff
Abdellatif Khelil, Larbi Talbi, Djamel Slimani, J. LeBel

Bibliographic record

VenueInternational Journal of Sensors Wireless Communications and Control · 2019
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsTransmitterChannel (broadcasting)Computer scienceTransmission (telecommunications)Non-line-of-sight propagationFadingWirelessMATLABElectronic engineeringFrequency-division multiple accessOrthogonal frequency-division multiplexingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Aims: This paper presents an investigation of image transmission using SC-FDMA system over wireless fading channels. Background: A comparison is performed between the performance of the wireless transmitted image over 3GPP LTE channel (vehicular and pederstain) and mmWaves measured channel at 29.5 GHz. Objective: One of the important benefits of this work is the real measurement obtained at 29.5 GHz under NLOS scenario using an original sounder based on the ensuring of phase coherence between the transmitter and the receiver. Methods: Cameraman image has been transmitted over the SC-FDMA with different subcarriers mapping schemes. PSNR and MSE are the metrics used to evaluate the performance of the image transmitted under Matlab simulation where their values of the received image are calculated for different SNR values. Results and Conclusion: : The simulation results show that the image transmission over mmWaves channel works and the best values of PSNR are attained beyond 12 dB and 14 dB for LSC-FDMA and ISC-FDMA respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.605

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 science0.0010.000
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 head0.016
GPT teacher head0.241
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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