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Record W2125647196 · doi:10.1109/isssta.2000.878087

Performance enhancement of DS-CDMA system using overlapping sectors with interference avoidance

2002· article· en· W2125647196 on OpenAlexaff
B.K. Ng, E. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterference (communication)Code division multiple accessComputer scienceTransmission (telecommunications)Selection (genetic algorithm)TelecommunicationsCode (set theory)Electronic engineeringEngineeringChannel (broadcasting)Artificial intelligence

Abstract

fetched live from OpenAlex

A cellular system with overlapping sectors is proposed to enhance system performance through interference avoidance. In such system, users lie within the coverage of more than one sector, yet each user only chooses one sector for signal transmission and reception. The choices of sectors among different users affect the interference level experienced by others. It is shown in this paper that their mutual interference is minimized by choosing a good sector combination. We investigate the potential benefits of proposed technique in two scenarios: an orthogonal and a non-orthogonal CDMA system. In the former case, spreading code allocation combined with sector selection reduces the required number of code to support all users compared to conventional non-overlapping sector system. For the non-orthogonal CDMA system, the enhancement takes the form of multiple access interference reduction. Simulations results show that its improvement is pronounced and can be further enhanced by increasing the number of overlapping sectors.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0020.001
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 head0.051
GPT teacher head0.254
Teacher spread0.202 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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