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Record W2332997872 · doi:10.9766/kimst.2013.16.6.819

The Design and Performance Analysis of Synchronization on Frequency Hopping Network Communication System

2013· article· en· W2332997872 on OpenAlexfundno aff
Sojin Lim, Suk-Neung Bae, Sung-Woo Han

Bibliographic record

VenueJournal of the Korea Institute of Military Science and Technology · 2013
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsFrequency-hopping spread spectrumsyncSynchronization (alternating current)Additive white Gaussian noiseElectronic engineeringBandwidth (computing)Computer scienceCommunications systemNetwork packetComputer networkTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Compared to legacy frequency hopping communications, future radio communications are required the secure and high data rate, ad-hoc network communication. In this paper, we have designed the network communication structure on the frequency hopping mode, and analyzed the performance of synchronization on the frequency hopping network radio systems. The design results are shown the initial sync. phase of approximately 9 hops and the traffic packet phase of approximately 30 hops. Also, we have simulated the performance on the communication conditions which are carrier bandwidth of 50kHz, user data rate of 64kbps and OQPSK modulation scheme in AWGN. In the simulation, we analyzed the correlation and the performance of synchronization success. The result of simulation show 99% probability for synchronization success at $E_b/N_o$ -4dB.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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