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Record W2154904432 · doi:10.1109/icc.2005.1495023

Effect of channel variation in IP/CDMA2000 interconnection performance

2005· article· en· W2154904432 on OpenAlexaff
Vikas Paliwal, P. Larijani, Ioannis Lambadaris, B. Nandy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceComputer networkBandwidth (computing)Queueing theoryQueueRouterNetwork packetBandwidth allocationChannel allocation schemesWirelessDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

In order to support high data rate requirements and effectively manage scarce wireless resources, additional bandwidth channels are quite frequently allocated and taken away from mobile stations in 3G wireless data networks. A TCP sender connected to the mobile, on seeing ACKs coming at a faster pace after additional bandwidth allocation, turns overtly optimistic and injects data into the network in a more bursty manner that might be excessive for an intermediate router, thereby leading to loss of multiple packets and subsequent prolonged recovery and periods of underutilization. We characterize this problem using an analytical model for losses based on a continuous flow approximation as well as an extensive simulation setup. We also illustrate how bandwidth oscillations create more severe congestion than an increase in the number of users to the extent that even the RED algorithm is unable to check the sharp growth of queues. As a result, multiple packets are lost in a droptail fashion. We further demonstrate the dependence of congestion due to bandwidth allocation on the time during which mobiles' rates are increased and observe the degradation in performance for typical load scenarios.

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)0.000
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: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

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