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An algorithm for optimal secure signaling over cognitive radio MIMO channels

2017· article· en· W2791803390 on OpenAlexaff
Limeng Dong, Sergey Loyka, Yong Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMIMOCognitive radioBacktrackingComputer scienceAlgorithmMaximizationConstraint (computer-aided design)Channel (broadcasting)Interference (communication)Transmitter power outputTransmitterMathematical optimizationWirelessMathematicsTelecommunications

Abstract

fetched live from OpenAlex

The multiple-input multiple-output (MIMO) Gaussian wire-tap channel (WTC) model is considered in the cognitive radio (CR) setting, where there is a interference power constraint (IPC), in addition to the total transmit power constraint (TPC). Based on the recently established mini-max characterization of its secrecy capacity, a numerical algorithm for global maximization of secrecy rates over such channels is proposed, which is based on the barrier method in combination with the residual-form Newton method and backtracking line search. Unlike the known algorithms, the proposed algorithm is guaranteed to converge to a global (rather than local) optimum. Its efficient performance is illustrated by examples.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.673

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 communication0.0000.001
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 head0.023
GPT teacher head0.300
Teacher spread0.277 · 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 designOther design
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

Citations4
Published2017
Admission routes1
Has abstractyes

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