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A New Way to Integrate Physics-Based Channel Models in Communication System Design

2018· article· en· W2908959188 on OpenAlexaff
Neeraj Sood, Xingqi Zhang, Sami Baroudi, Jörg Liebeherr, Costas D. Sarris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProtocol designComputer scienceChannel (broadcasting)Simple (philosophy)WirelessProtocol (science)Systems designCommunications protocolPoint (geometry)Wireless networkNetwork planning and designCommunications systemDistributed computingComputer networkSystems engineeringTelecommunicationsEngineeringSoftware engineering

Abstract

fetched live from OpenAlex

Wireless communication system design is traditionally carried out in a rather sequential manner. As a result, propagation modeling and network protocol design have largely evolved as independent areas. This conventional fragmented approach does not fully capitalize on potential efficiencies during the design of communication systems. This paper proposes an integrated approach for communication system design, which is illustrated using an example of optimizing access point locations in a curved rectangular tunnel. The simple yet insightful example demonstrates the benefits of co-developing the wireless channel models and network protocol design and hence paves the way for further develooment of this intearated aooroach.

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: Methods · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.452

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 science0.0010.000
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 head0.045
GPT teacher head0.255
Teacher spread0.210 · 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
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

Citations3
Published2018
Admission routes1
Has abstractyes

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