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Record W2945629519 · doi:10.1109/lcomm.2019.2917684

Fast-OFDM With Index Modulation for NB-IoT

2019· article· en· W2945629519 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Communications Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingSpectral efficiencyComputer scienceModulation (music)Electronic engineeringBandwidth (computing)NarrowbandConstellationBit error rateTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

In this letter, a hybrid orthogonal frequency-division multiplexing (OFDM)-based modulation technique for narrowband Internet of Things (NB-IoT) is introduced and analyzed. The technique combines fast-OFDM with index modulation in order to maximize bandwidth and power efficiency for IoT applications. The ideal number of active subcarriers to maximize spectral efficiency is derived. The one-dimensional constellation used in fast-OFDM is also optimized to enhance error performance of the proposed system. Numerical results indicate that the proposed system outperforms other OFDM systems based on index modulation in the relatively low signal-to-noise ratio (SNR) region, while it provides additional design options for trading off power efficiency and spectral efficiency in the higher SNR region.

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.

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 score0.727
Threshold uncertainty score0.663

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 head0.240
Teacher spread0.224 · 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