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Record W2164148163 · doi:10.1109/icc.2001.937144

Integrated rate and error control in variable spreading gain WCDMA systems

2002· article· en· W2164148163 on OpenAlexaff
Dong In Kim, Ekram Hossain, V.K. Bhargava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCellular networkComputer scienceCode division multiple accessTelecommunications linkNetwork packetTransmission (telecommunications)Computer networkInterference (communication)WidebandWireless networkChannel (broadcasting)WirelessElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Optimal dynamic rate allocation among mobile stations for variable rate packet data transmission in a cellular wireless network is an NP-complete problem; therefore, sub-optimal solutions to this problem are sought. Again, interference calculation is non-trivial in the case of a packet-switched cellular CDMA network with heterogeneous traffic load in different cells. In this paper, a sub-optimal two-step dynamic rate selection procedure is proposed for uplink packet data transmission in cellular WCDMA (wideband code division multiple access) networks. A novel 'mean-sense' approach for inter-cell interference calculation is employed assuming homogeneous traffic load in the different cells. Two different error control alternatives for this variable rate packet transmission environment are presented and their performances are analyzed for three different channel models. The performance of the proposed two-step rate selection procedure is fairly close to that of the optimal rate allocation found through exhaustive search.

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 score0.981
Threshold uncertainty score0.350

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 science0.0010.000
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 head0.041
GPT teacher head0.263
Teacher spread0.222 · 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

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