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Record W2117467102 · doi:10.1109/twc.2008.060626

On antijamming in general CDMA systems-part I: multiuser capacity analysis

2008· article· en· W2117467102 on OpenAlexaff
Reza Nikjah, Norman C. Beaulieu

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2008
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFadingTelecommunications linkComputer scienceChannel state informationJammingNakagami distributionChannel capacityBandwidth (computing)Computer networkSpread spectrumChannel (broadcasting)Code division multiple accessTelecommunicationsWireless

Abstract

fetched live from OpenAlex

The capacity of a general multiuser CDMA jamming channel is analyzed when the receiver has or lacks jammer state information. Analyses are carried out for noncooperative and cooperative users in uplink and downlink static and Nakagami fading channels. The results are applied to a versatile multiaccess channel model which is based on the time-bandwidth dimensionality and is considered for the user capacity analysis of a variety of multiuser spread spectrum systems contaminated by jamming. In this model, different users are distinguished by signatures of either direct sequence or hopping type. It is found that the jammer should spread its energy evenly over all degrees of freedom in order to minimize the average capacity. Also, the capacity behavior appears to be dominated more by knowledge of jammer state information than by the effects of fading. The capacity of downlink channels in cooperative schemes is found to be more sensitive to changes in fading severity, compared with the capacity of other kinds of channels.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.074
GPT teacher head0.299
Teacher spread0.225 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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