MétaCan
Menu
Back to cohort
Record W2130824264 · doi:10.1109/icc.1995.524233

Concatenated orthogonal/PN spreading scheme for cellular DS-CDMA systems with integrated traffic

2002· article· en· W2130824264 on OpenAlexaff
Mo-Han Fong, V.K. Bhargava, Q. Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceCode division multiple accessHadamard transformComputer networkMultipath propagationAlgorithmInterference (communication)FadingReal-time computingDecoding methodsMathematics

Abstract

fetched live from OpenAlex

The application of the concatenated orthogonal/PN spreading scheme for integrated traffic is introduced. Bhargava (1994) proposed using a single line rate (adjusted data rate before spreading) to accommodate traffic with a wide range of source rates. For traffic with source rates higher than the line rate, the authors propose using concatenated orthogonal/PN spreading sequences to subdivide a high rate stream into several parallel line rate streams. The performance of the concatenated orthogonal-PN spreading sequence for homogenous voice traffic in various cellular mobile environments with multipath fading, log-normal shadowing and path loss, is first analyzed and compared with that of the conventional non-concatenated long PN sequence. The authors then evaluate the performance of a system with integrated traffic of voice and video. In conjunction with this, they propose the use of cosets of Walsh-Hadamard (WH) codes to reduce the multi-user interference. Different methods of assigning the spreading sequences among the cosets which improve the capacity of both voice and video users, are investigated.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.663

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 communication0.0000.000
Open science0.0020.000
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 head0.046
GPT teacher head0.246
Teacher spread0.200 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207