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Record W2160998941 · doi:10.1109/vetecf.2003.1285287

Downlink scheduling for CDMA packet data systems with interference cancellation

2003· article· en· W2160998941 on OpenAlexaff
Amine Maaref, Sonia Aı̈ssa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsTelecommunications linkComputer scienceScheduling (production processes)Computer networkSingle antenna interference cancellationQueueing theoryNetwork packetCode division multiple accessThroughputMaximum throughput schedulingReal-time computingRound-robin schedulingFair-share schedulingWirelessTelecommunicationsQuality of serviceEngineering

Abstract

fetched live from OpenAlex

We propose a downlink packet scheduling algorithm for cells of multi-rate CDMA networks in the presence of a limited interference cancellation (IC) capability at the mobile units with in-cell transmission power and out-of-cell interference limitations. By targeting the most interfering transmissions within the adjacent sectors for suppression, we show that proper combination of scheduling and IC can significantly enhance the performance of CDMA packet data downlinks both in terms of fairness and throughput, thereby, reducing queueing and transmission delays. With a limited IC capability of about 70%, results show that fairness to distant mobiles can be ensured with a per-user throughput twice as high as the throughput obtained when IC is not combined with scheduling.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.120
GPT teacher head0.334
Teacher spread0.214 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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