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Record W2148574559 · doi:10.1109/wcnc.1999.796976

Transmission of high speed data in cdma2000

2003· article· en· W2148574559 on OpenAlexaff
Nan Guo, Farideh Khaleghi, A. Gutierrez, Jiaxuan Li, Mo-Han Fong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsCDMA2000Computer scienceParameterized complexityPhysical layerCode division multiple accessWirelessFrame (networking)Transmission (telecommunications)Convolutional codeComputer networkData transmissionData as a serviceCode (set theory)Mobile telephonyMobile radioReal-time computingTelecommunicationsDecoding methodsAlgorithmSet (abstract data type)

Abstract

fetched live from OpenAlex

Requirements for CDMA high-speed data (HSD) services make one of the distinguish marks between second generation and third generation (3G) wireless systems. cdma2000 provides 3G capacity, high data rates of up to 2 Mb/s and services. This paper describes a generalized frame structure for the support of sub-frames within cdma2000. This structure is parameterized such that it can be configured for efficient operation, depending on the radio environment and mobile station complexity, in order to provide several degrees of freedom. Impact on both convolutional code and turbo-code physical layer implementation is investigated. The results show the benefits of using the sub-frames technique.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.272
Teacher spread0.247 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations12
Published2003
Admission routes1
Has abstractyes

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