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Record W2611502061 · doi:10.1109/wcncw.2017.7919087

Numerical Evaluation of Information Outage for BPSK FHSS Link Performance Analysis

2017· article· en· W2611502061 on OpenAlexfundno aff
Hendrik Lieske, Sebastian Rauh, Albert Heuberger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsFadingPhase-shift keyingAdditive white Gaussian noiseComputer scienceChannel (broadcasting)Frequency-hopping spread spectrumSpread spectrumDecoding methodsMetric (unit)Mutual informationElectronic engineeringAlgorithmTelecommunicationsBit error rateEngineering

Abstract

fetched live from OpenAlex

The information outage probability serves as a practical benchmark for the performance evaluation of channel codes in the finite block length region. The metric is derived from the distribution of the mutual information between a channel's in- and output and is readily available for the Gaussian AWGN channel. To review its applicability for modulation constraint channel inputs, we propose a numerical method for the evaluation of the mutual information random variable for BPSK modulated code words. We apply the model to a block fading channel to study the link performance of a frequency hopping spread spectrum (FHSS) system in a slow-fading two-path propagation environment, which is a typical scenario for low power wide area (LPWA) applications. The information outage probability closely reproduces the behavior of a practical rate 1/3 Turbo decoder.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.327
Teacher spread0.292 · 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
Published2017
Admission routes1
Has abstractyes

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