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Secure Communication with Physical Layer Coding Scheme

2023· article· en· W4386323222 on OpenAlexaff
Jong-Shin Chen, Cheng‐Ying Yang, Jenq-Foung Yao, Chin-En Yen, Min‐Shiang Hwang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsEducation and Early Childhood Development
FundersMinistry of Science and Technology
KeywordsCommunication sourceComputer scienceSecrecyComputer networkEncryptionComputer securitySecure communicationPhysical layerDecoding methodsCryptographyInformation-theoretic securityMessage authentication codeSecure codingInformation securityWirelessTelecommunicationsSecurity service

Abstract

fetched live from OpenAlex

For a secure communication, it enables the destination could successfully release the authentic information from the sender. At the meantime, a secure system protects the transmitted information from the eavesdroppers. Traditionally, the encryption, authorization and authentication schemes could be employed for protecting the eavesdroppers. However, the unexpected attacks have been continuously developed. It could not be avoided for the undesired attacks. According to Shannon's perfect communication, there exists an error-free cryptogram that could protect for the attacker. Hence, based on the secrecy rate, it is expected that the secure communication could be practical with physical-layer coding scheme. Besides, the coding scheme is not only to obtain a critical level of security, but also to achieve a lower decoding error. For a security concern, increasing the secrecy rate leads a higher level of security ability. However, it might suffer the system performance without bandwidth efficiency. Hence, the tradeoff between the security level and coding rate is important to lead a secure communication.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.261
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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