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Record W2136254992 · doi:10.1155/asp.2005.183

Cross-Layer Resource Allocation for Variable Bit Rate Multiclass Services in a Multirate Multicarrier DS-CDMA Network

2005· article· en· W2136254992 on OpenAlexaff
J.W. Mark, Kee-Chaing Chua

Bibliographic record

VenueEURASIP Journal on Advances in Signal Processing · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceQuality of serviceComputer networkNetwork packetCode division multiple accessResource allocationLink layerPhysical layerBit error rateChannel (broadcasting)TelecommunicationsWireless

Abstract

fetched live from OpenAlex

An approximate analytical formulation of the resource allocation problem for handling variable bit rate multiclass services in a cellular round-robin carrier-hopping multirate multicarrier direct-sequence code-division multiple-access (MC-DS-CDMA) system is presented. In this paper, all grade-of-service (GoS) or quality-of-service (QoS) requirements at the connection level, packet level, and link layer are satisfied simultaneously in the system, instead of being satisfied at the connection level or at the link layer only. The analytical formulation shows how the GoS/QoS in the different layers are intertwined across the layers. A novelty of this paper is that the outages in the subcarriers are minimized by spreading the subcarriers' signal-to-interference ratio evenly among all the subcarriers by using a dynamic round-robin carrier-hopping allocation scheme. A complete sharing (CS) scheme with guard capacity is used for the resource sharing policy at the connection level based on the mean rates of the connections. Numerical results illustrate that significant gain in the system utilization is achieved through the joint coupling of connection/packet levels and link layer.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.763
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0020.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.348
Teacher spread0.321 · 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.

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
Published2005
Admission routes1
Has abstractyes

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