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Deep Learning for an Anti-Jamming CPM Receiver

2019· article· en· W3012137893 on OpenAlexaff
Yang Qiu, Colin Brown, L. Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsCommunications Research Centre CanadaGovernment of Canada
Fundersnot available
KeywordsJammingComputer scienceWaveformBoosting (machine learning)Synchronization (alternating current)Bit error rateTone (literature)Frequency-hopping spread spectrumContinuous phase modulationElectronic engineeringAlgorithmArtificial intelligenceTelecommunicationsEngineeringChannel (broadcasting)Decoding methodsPhysics

Abstract

fetched live from OpenAlex

A novel continuous phase modulation (CPM) receiver model is proposed in this paper that employs deep learning (DL) techniques to improve the signal recovery and synchronization performance under heavy jamming conditions for frequency hopping (FH) based waveforms. A hybrid deep neural network is used to implement the DL in the receiver model. The simulation results show that the proposed receiver with DL boosting is robust under tone jamming which is a worst case scenario for a CPM receiver. The model achieves 3 - 5dB improvement under single-tone jamming, in terms of bit-error-rate (BER), and 2dB improvement under multi-tone jamming, compared with a receiver without DL.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.266
Teacher spread0.236 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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