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Record W2075416647 · doi:10.1109/tcomm.2015.2409259

Jitter-Robust Spectral Shaping in OFDM

2015· article· en· W2075416647 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJitterOrthogonal frequency-division multiplexingElectronic engineeringComputer scienceReduction (mathematics)Sampling (signal processing)Spectral efficiencyControl theory (sociology)MathematicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Timing jitter in OFDM systems results in non-ideal sampling and pulse-shaped transmission times and thus, is an important limiting factor for practical OFDM systems. In this paper, the effect of sampling jitter on the spectrum of a transmitted OFDM signal is considered. This effect in particular can lead to a significant degradation in the performance of spectral shaping techniques in OFDM, e.g., out-of-band radiation reduction techniques, that are mostly based on the assumption of ideal sampling times and accurate synchronization. In this work, we first evaluate the effect of random sampling time jitter on the performance of the active interference cancellation (AIC) technique, a well-known out-of-band radiation reduction technique for OFDM. Then, using a minimax approach, we propose a robust scheme that takes the jitter effect into consideration and therefore, makes the sidelobe suppression technique robust against worst-case random jitter. Numerical simulations show a performance improvement of almost 3 dB in interference reduction for our proposed robust scheme in the presence of timing jitter.

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 score0.891
Threshold uncertainty score0.719

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 science0.0010.000
Research integrity0.0000.001
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 head0.141
GPT teacher head0.294
Teacher spread0.153 · 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