论文标题
理论上稀疏秘密一代的新颖折衷
A Theoretically Novel Trade-off for Sparse Secret-key Generation
论文作者
论文摘要
在本文中，我们从理论上解决了基于费率的稀疏字典学习问题。我们表明，有兴趣计算的自由度（DOF）为最小设置计算的$ - $卫星，以保证我们的利率 - 差异权衡$ - $基本上可以通过\ textit {langevin}方程访问。我们确实探讨了DOF的相对时间演变，即过渡跳跃是放松相对优化问题的基本问题。随后，我们通过\ textit {graphon}原理W.R.T.证明了上述放松。随机\ textIt {chordal schramm-loewner}进化等\ Mathscr {g} _1，\ Mathscr {g} _2} {\ rm \; } \ Mathcal {d} \ big（t \ big（\ mathscr {g} _1，\ Mathscr {g} \ big），t \ big（\ mathscr {g} _2，\ mathscr {g}我们还将场景扩展到窃听案例。我们最终通过模拟证明了我们提出的方案的效率。
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 \textit{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 \textit{Graphon} principle w.r.t. a stochastic \textit{Chordal Schramm-Loewner} evolution etc {via a minimisation over a distortion between the relative realisation times of two given graphs $\mathscr{G}_1$ and $\mathscr{G}_2$ as $ \mathop{{\rm \mathbb{M}in}}\limits_{ \mathscr{G}_1, \mathscr{G}_2} {\rm \; } \mathcal{D} \Big( t\big( \mathscr{G}_1 , \mathscr{G} \big) , t\big( \mathscr{G}_2 , \mathscr{G} \big) \Big)$}. We also extend our scenario to the eavesdropping case. We finally prove the efficiency of our proposed scheme via simulations.
