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Record W2170635816 · doi:10.1109/tvt.2007.905606

Evaluations of Achievable Rate and Power Consumption in Cooperative Cellular Networks With Two Classes of Nodes

2008· article· en· W2170635816 on OpenAlexaff
Ki-Dong Lee, Victor C. M. Leung

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2008
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRelayComputer networkBase stationPower consumptionComputer scienceTransmitter power outputHomogeneousPower (physics)Cellular networkTransmitterMathematicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Previous work on cooperative cellular networks has mostly considered homogeneous relaying architectures where all nodes act as both sources and relays. In this paper, we examine the performance of heterogeneous cooperative cellular networks with the following two classes of nodes: source nodes (SNs) that do not act as relays and relaying agents that are dedicated to relaying functions with little concern about power consumption. In this architecture, SNs are able to reap the benefits of cooperative communications, such as improvements in the achievable data rate and reductions in the transmit power, while reducing the overall power consumption since they do not act as a relay. With an SN employing no more than two parallel relaying agents between itself and the base station, the proposed architecture achieves a 10%-80% improvement in the average achievable rate and a 15%-30% savings in the average amount of consumed power as compared to the situation where an SN can use, at most, a single relay agent.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.276
Teacher spread0.248 · 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

Citations13
Published2008
Admission routes1
Has abstractyes

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