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Record W1507732015 · doi:10.1109/infcom.1998.662929

A generalized processor sharing approach to time scheduling in hybrid CDMA/TDMA

2002· article· en· W1507732015 on OpenAlexaff
Mohammad A. Arad, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTime division multiple accessComputer scienceComputer networkScheduling (production processes)Quality of serviceCode division multiple accessPower controlWirelessAccess controlDistributed computingPower (physics)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Future wireless ATM networks are expected to support a variety of services with different bit-rates and quality-of-service (QoS) requirements. A major challenge for these networks is the design of the multiple access scheme. In our previous works, we proposed the hybrid CDMA/TDMA technique as an appropriate multiple access control (MAC) scheme for wireless ATM networks. We also investigated the problem of power control in such hybrid systems. In this paper, we continue our study and address the time scheduling issue of hybrid CDMA/TDMA. Using the generalized processor sharing technique, typically employed in TDMA systems, we develop a time scheduling scheme suitable for the class of hybrid CDMA/TDMA systems. We also study the implementation of this scheme in a specific hybrid system we proposed earlier and address its integration with our power control 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.282
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 source (direct Gemma or distilled Codex), 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

Citations73
Published2002
Admission routes1
Has abstractyes

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