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Record W2618961171 · doi:10.1109/syscon.2017.7934716

Performance of constant envelope DCT based OFDM system with M-ary PAM mapper in AWGN channel

2017· article· en· W2618961171 on OpenAlexaff
Rayan Hamza Alsisi, Raveendra K. Rao

Bibliographic record

Venue2017 Annual IEEE International Systems Conference (SysCon) · 2017
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsWestern University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingAdditive white Gaussian noiseDiscrete cosine transformBit error rateAmplifierElectronic engineeringComputer scienceModulation (music)Channel (broadcasting)AlgorithmTelecommunicationsEngineeringAcousticsPhysicsCMOS

Abstract

fetched live from OpenAlex

Constant envelope discrete cosine transform based Orthogonal Frequency Divination Multiplexing (CE-DCT-OFDM) system is presented. The performance of such a system is examined over AWGN channel for transmission of data using M-ary pulse amplitude modulation (M-ary PAM) mapper. In the system, phase modulation (PM) is used to overcome the problem of high peak-to-average power that is typical in conventional DCT-OFDM systems. As a result the system permits high power amplifier to operate near saturation level and thus offers maximum power efficiency. Closed-form expression for bit error rate of the system is derived, illustrated, and compared to simulation results. Also, bit error rate performance of CE-DCT-OFDM and conventional DCT-OFDM systems are compared as a function of IBO and SNR using traveling-wave tube amplifier (TWTA) model. It is observed that CE-DCT-OFDM system offers a variety of advantages over conventional DCT-OFDM system.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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
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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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Same venue2017 Annual IEEE International Systems Conference (SysCon)Same topicPAPR reduction in OFDMFrench-language works237,207