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Record W2106377167 · doi:10.1109/twc.2006.1611082

Effective bandwidth of multimedia traffic in packet wireless CDMA networks with LMMSE receivers: a cross-layer perspective

2006· article· en· W2106377167 on OpenAlexaff
F. Richard Yu, Vikram Krishnamurthy

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2006
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkPhysical layerBandwidth (computing)Code division multiple accessNetwork packetQuality of serviceWireless networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

We propose a novel concept of cross-layer effective bandwidth that characterizes the unified resource usage taking into account both physical layer linear minimum mean square error (LMMSE) receivers and statistical characteristics of the packet traffic in code division multiple access (CDMA) networks. Based on the concept of cross-layer effective bandwidth, we develop an optimal connection admission control (CAC) scheme for variable bit rate packet traffic with QoS constraints at both physical and network layers. By introducing a small signal-to-interference ratio (SIR) outage probability using the concept of cross-layer effective bandwidth, the capacity of CDMA networks in the proposed CAC scheme can be increased significantly compared to some existing schemes. The effectiveness of the proposed approaches is demonstrated by 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)0.004
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.297
Teacher spread0.277 · 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

Citations31
Published2006
Admission routes1
Has abstractyes

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