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Record W2028268175 · doi:10.1109/iswcs.2012.6328507

On the robustness of oversampled filter bank multi carrier systems against frequency offset

2012· article· en· W2028268175 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsMcGill University
Fundersnot available
KeywordsOversamplingCarrier frequency offsetAdditive white Gaussian noiseRobustness (evolution)Bit error rateComputer scienceFilter bankElectronic engineeringFrequency offsetAlgorithmOrthogonal frequency-division multiplexingTelecommunicationsBandwidth (computing)White noiseEngineeringDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we study the effect of oversampling in a perfect reconstruction (PR) filter bank multi carrier (FBMC) system recently proposed by the authors. Particularly, we investigate the performance of this system in the presence of carrier frequency offset (CFO). We show that the CFO introduces interference components in the receiver. By exploiting the statistical properties of the received subband signals, the average of the signal-to-interference ratio (SIR) is derived to exhibit the tradeoff between performance and efficiency. Furthermore, bit-error-rate (BER) comparisons of FBMC systems with different oversampling ratios over frequency-selective and additive white Gaussian noise (AWGN) channels in the presence of CFO are presented. These results confirm that oversampling increases robustness of the system against CFO.

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: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.559

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.231
Teacher spread0.201 · 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

Quick stats

Citations5
Published2012
Admission routes1
Has abstractyes

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