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Record W1912541695 · doi:10.1109/icc.2000.853650

Effects of correlated interference on the potential linear antenna gain in CDMA macrodiversity systems

2002· article· en· W1912541695 on OpenAlexaff
Halim Yanıkömeroğlu, E.S. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of TorontoCarleton University
Fundersnot available
KeywordsInterference (communication)Bandwidth (computing)Computer scienceCode division multiple accessCommunications systemComputer networkElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

It is reported by Hanly (see IEEE Trans. Commun., vol.44, no.2, p.247-56, 1996) that in the reverse link of a CDMA macrodiversity system a remarkable L-fold capacity (throughput) increase can be attained by using L antenna elements (AEs) provided that the spread spectrum bandwidth approaches infinity. In a finite-bandwidth system, however, the increase in capacity as a result of the utilization of multiple AEs will be less than linear due to the presence of the correlated interference effects. In this paper, a spatial correlated interference analysis is presented in order to investigate the effects of the system parameters on the severity of the correlated interference. The results presented indicate that the parameter which we defined as the chiplength ([speed of light]/[chip rate]) plays a role in macrodiversity systems (in regards to the correlation effects) similar to the role of the carrier wavelength in microdiversity systems. It is observed that in systems where the size of the service region is large enough to enable inter-AE distances many times larger than the chiplength, significant (close to linear) capacity gains can be achieved by employing macrodiversity with many AEs. If, on the other hand, the service region size is not large enough with respect to the chiplength, the returns due to macrodiversity will not be as high.

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 score0.804
Threshold uncertainty score0.379

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 communication0.0000.000
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 head0.026
GPT teacher head0.236
Teacher spread0.210 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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