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Record W2477505821 · doi:10.1017/cbo9781107478732.008

Cross-layer design for device-to-device communication

2015· book-chapter· en· W2477505821 on OpenAlexaff
Lingyang Song, Dusit Niyato, Ekram Hossain

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook-chapter
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceLayer (electronics)Flexibility (engineering)Modular designProtocol (science)Computer networkCommunications protocolDistributed computingApplication layerOSI modelTransmission (telecommunications)WirelessComputer architectureTelecommunicationsOperating systemNanotechnology

Abstract

fetched live from OpenAlex

Traditional communication systems were built using a layered structure to provide well-defined but limited interfaces among protocols in adjacent layers. The modular design of the layered structure, where the details of each protocol are hidden, promotes the interoperatability of the communication protocols. Although the protocol details (e.g., states and internal functions) are encapsulated in each layer, this structure prevents the protocols sharing, accessing, and controlling the operations of other protocols, which might be required for efficient data transmission. Therefore, the concept of cross-layer design has been introduced to allow tighter integration of different protocols. It is not necessary for these protocols to be in adjacent layers. The benefits of cross-layer design are that it allows one to improve both the flexibility of protocol implementations and network performance. Cross-layer design and optimization have been adopted in various wireless systems. A few surveys on cross-layer design exist in the literature, e.g., [190, 191, 192, 193].

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.002
Research integrity0.0010.000
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 head0.127
GPT teacher head0.297
Teacher spread0.170 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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