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Record W2567001250 · doi:10.1109/camad.2016.7790339

Detection of M-ary OFDM systems with CPM mapper over multipath channels

2016· article· en· W2567001250 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 institutionsWestern University
Fundersnot available
KeywordsAdditive white Gaussian noiseOrthogonal frequency-division multiplexingMultipath propagationComputer scienceAlgorithmChannel (broadcasting)Continuous phase modulationModulation (music)Electronic engineeringTelecommunicationsEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

In this paper, a class of OFDM systems with Continuous Phase Modulation (CPM) mapper signals is discussed and theoretical predictions for symbol error probabilities are derived, where the memory is employed to improve system performance. Previously, results summarized that binary data of OFDM systems with CPM mapper is mapped with complex symbols using the concept of correlated phase states of CPM signal. The results presented in this paper show that M-ary OFDM systems with CPM mapper outperforms the conventionally used M-ary memory-less mapper in OFDM systems. Optimum and suboptimum multiple-symbol observation OFDM systems with CPM mapper receivers are derived. Multipath channel with Additive White Gaussian Noise (AWGN) is assumed. Also, symbol error rate performance in terms of high and low SNR bounds is analyzed and assessed in terms of the value of the deviation ratio h, time delay, and attenuation level. This paper provides a complete analysis of the performance of the OFDM systems with CPM mapper at high SNR as well as low SNR and as a result unifies and extends the previously available results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.259

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)0.0000.000

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 head0.007
GPT teacher head0.184
Teacher spread0.176 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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