MétaCan
Menu
Back to cohort
Record W2126468174 · doi:10.1109/pimrc.1995.476874

Performance bounds evaluation of FH SS radio networks with interference modeled as a mixture of Gaussian and alpha-stable noise

2002· article· en· W2126468174 on OpenAlexaff
Jacek Ilow, Dimitrios Hatzinakos, A.N. Venetsanopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFrequency-shift keyingGaussian noiseTransmitterNoise (video)GaussianInterference (communication)Random variableTelecommunicationsComputer scienceAlgorithmMathematicsElectronic engineeringTopology (electrical circuits)PhysicsStatisticsEngineeringArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

We consider the performance of frequency-hopping spread spectrum (FH SS) radio networks in a Poisson field of interfering terminals using the same modulation and power. The problem is relevant to mobile communication systems where the mobility of users requires random modeling of transmitter positions in the network. Assuming logarithmic attenuation of the signal strength over distance between the transmitter and receiver, we show that the interference in the network could be modeled as a mixture of Gaussian and circularly symmetric /spl alpha/-stable noise. Based on this model, we derive union bound approximations for the probability of error for FH systems with M-ary frequency shift keying (FSK). We generalize some of the results of Sousa (see IEEE Trans. I.T., vol.38, no.6, p.1743, 1992), where the focus was limited to Cauchy random variables (RVs), a special subclass of stable distributions, and where the effect of a background (Gaussian) noise was neglected. Numerical calculations and Monte Carlo simulations confirm the accuracy of our analysis. The results obtained allow the prediction of wireless system performance in environments varying from urban settings to office buildings.

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 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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.039
GPT teacher head0.272
Teacher spread0.233 · 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 teacher head, 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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207