A Theoretically Novel Trade-off for Sparse Secret-key Generation
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We in this paper theoretically go over a rate-distortion based sparse dictionary learning problem. We show that the Degrees-of-Freedom (DoF) interested to be calculated - satnding for the minimal set that guarantees our rate-distortion trade-off - are basically accessible through a Langevin equation. We indeed explore that the relative time evolution of DoF, i.e., the transition jumps is the essential issue for a relaxation over the relative optimisation problem. We subsequently prove the aforementioned relaxation through the Graphon principle w.r.t. a stochastic Chordal Schramm-Loewner evolution etc via a minimisation over a distortion between the relative realisation times of two given graphs 𝒢_1 and 𝒢_2 as 𝕄in_𝒢_1, 𝒢_2 𝒟( t( 𝒢_1 , 𝒢) , t( 𝒢_2 , 𝒢) ). We also extend our scenario to the eavesdropping case. We finally prove the efficiency of our proposed scheme via simulations.
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