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Record W2093719695 · doi:10.1155/wcn/2006/21297

Cross-Layer Design and Analysis of Downlink Communications in Cellular CDMA Systems

2006· article· en· W2093719695 on OpenAlexaff
Jin Sun, Lian Zhao, Alagan Anpalagan

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2006
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceTelecommunications linkComputer networkData link layerPhysical layerTransport layerNetwork layerThroughputScheduling (production processes)Wireless networkCode division multiple accessLink layerSpurious relationshipPower controlLayer (electronics)Network performanceHandoverApplication layerWirelessCellular networkPower (physics)Telecommunications

Abstract

fetched live from OpenAlex

A cellular CDMA network with voice and data communications is considered. Focusing on the downlink direction, we seek for the overall performance improvement which can be achieved by cross-layer analysis and design, taking physical layer, link layer, network layer, and transport layer into account. We are concerned with the role of each single layer as well as the interaction among layers, and propose algorithms/schemes accordingly to improve the system performance. These proposals include adaptive scheduling for link layer, priority-based handoff strategy for network admission control, and an algorithm for the avoidance of TCP spurious timeouts at the transport layer. Numerical results show the performance gain of each proposed scheme over independent performance of an individual layer in the wireless mobile network. We conclude that the system performance in terms of capacity, throughput, dropping probability, outage, power efficiency, delay, and fairness can be enhanced by jointly considering the interactions across layers.

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)0.003
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: none
Teacher disagreement score0.716
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0040.002
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 head0.078
GPT teacher head0.335
Teacher spread0.257 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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