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Record W4254474339 · doi:10.1002/dac.887

Joint packet scheduling and dynamic base station assignment for CDMA data networks

2007· article· en· W4254474339 on OpenAlexaff
Christian Makaya, Sonia Aı̈ssa

Bibliographic record

VenueInternational Journal of Communication Systems · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à MontréalPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceBase stationNetwork packetComputer networkScheduling (production processes)Code division multiple accessWirelessQueueReal-time computingDistributed computingTelecommunicationsMathematical optimization

Abstract

fetched live from OpenAlex

Abstract In current code division multiple access (CDMA) based wireless systems, a base station (BS) schedules packets independently of its neighbours, which may lead to resource wastage and the degradation of the system's performance. In wireless networks, in order to achieve an efficient packet scheduling, there are two conflicting performance metrics that have to be optimized: throughput and fairness. Their maximization is a key goal, particularly in next‐generation wireless networks. This paper proposes joint packet scheduling and BS assignment schemes for a cluster of interdependent neighbouring BSs in CDMA‐based wireless networks, in order to enhance the system performance through dynamic load balancing. The proposed schemes are based on sector subdivision in terms of average required resource per mobile station and utility function approach. The fairness is achieved by minimizing the variance of the delay for the remaining head‐of‐queue packets. Inter‐cell and intra‐cell interferences from scheduled packets are also minimized in order to increase the system capacity and performance. The simulation results show that our proposed schemes perform better than existing schemes available in the open literature. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.307
Teacher spread0.272 · 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
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

Citations0
Published2007
Admission routes1
Has abstractyes

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