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Record W2012750660 · doi:10.1109/iscc.2007.4381553

Directional Cell Breathing Based Reactive Congestion Control in WCDMA Cellular Networks

2007· article· en· W2012750660 on OpenAlexaff
Khaled A. Ali, Hossam S. Hassanein, Hussein T. Mouftah

Bibliographic record

VenueProceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · 2007
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsHandoverCellular networkComputer scienceComputer networkCode division multiple accessPower controlNetwork congestionTransmission (telecommunications)ThroughputChannel (broadcasting)WirelessTelecommunicationsPower (physics)Network packet

Abstract

fetched live from OpenAlex

In this paper, we introduce a reactive congestion control scheme for wideband CDMA (WCDMA) cellular networks and study its performance with respect to network throughput and call dropping rates. This scheme utilizes the idea of directional cell breathing (DCB), in which network cells are partitioned into N-sectors where each sector is served by a directional smart antenna. We propose a heuristic algorithm called directional cell breathing based-reactive congestion control (DCBB-RCC) that controls the transmission power of the common pilot channel (CPICH) such that the coverage area of a cell sector can dynamically be extended towards a nearby loaded sector or shrunk towards cell center for a loaded sector. Therefore, this mechanism activates a handoff procedure to shift some traffic of a loaded cell towards a lightly loaded cell. The effectiveness of our proposal is investigated through snap shot simulation using numerical examples.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.260
Teacher spread0.240 · 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

Citations21
Published2007
Admission routes1
Has abstractyes

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Same venueProceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and CommunicationsSame topicWireless Communication Networks ResearchFrench-language works237,207