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Record W1521892269 · doi:10.1109/spawc.2015.7227001

Filtered OFDM: A new waveform for future wireless systems

2015· article· en· W1521892269 on OpenAlexaff
Javad Abdoli, Ming Jia, Jianglei Ma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingCyclic prefixComputer scienceElectronic engineeringOrthogonal frequency-division multiple accessAsynchronous communicationInterference (communication)Fast Fourier transformWaveformTelecommunicationsAlgorithmEngineeringChannel (broadcasting)Radar

Abstract

fetched live from OpenAlex

A spectrally-localized waveform is proposed based on filtered orthogonal frequency division multiplexing (f-OFDM). By allowing the filter length to exceed the cyclic prefix (CP) length of OFDM and designing the filter appropriately, the proposed f-OFDM waveform can achieve a desirable frequency localization for bandwidths as narrow as a few tens of subcarriers, while keeping the inter-symbol interference/inter-carrier interference (ISI/ICI) within an acceptable limit. Enabled by the proposed f-OFDM, an asynchronous filtered orthogonal frequency division multiple access (f-OFDMA)/filtered discrete-Fourier transform-spread OFDMA (f-DFT-S-OFDMA) scheme is introduced, which uses the spectrum shaping filter at each transmitter for side lobe leakage elimination and a bank of filters at the receiver for inter-user interference rejection. Per-user downsampling and short fast Fourier transform (FFT) are used at the receiver to ensure a reasonable complexity of implementation. The proposed scheme removes the inter-user time-synchronization overhead required in the synchronous OFDMA/DFT-S-OFDMA. The performance of the asynchronous f-OFDMA is evaluated and compared with that of the universal-filtered OFDM (UF-OFDM), proposed in [1], [2].

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.235
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations354
Published2015
Admission routes1
Has abstractyes

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