Pinsker’s inequality

Pinsker’s Inequality是信息论中的一个不等式，通常用于量化两个概率分布之间的差异。这个不等式是由苏联数学家Mark Pinsker于1964年提出的。

考虑两个概率分布 (P) 和 (Q) 在同一样本空间上的概率密度函数，Pinsker’s Inequality可以表示为：

[ $D_{\text{KL}}(P\parallel Q)\ge \frac{1}{2}{\left(\int {\left(\sqrt{p(x)}-\sqrt{q(x)}\right)}^{2}dx\right)}^2$ ]

其中：

• ($D_{\text{KL}}(P\parallel Q)$) 是P和Q之间的$Kullback-Leibler$散度，表示两个概率分布之间的差异。
• ($p(x)$) 和 ($q(x)$) 分别是P和Q在样本点 ($x$) 处的概率密度函数。

Pinsker’s Inequality表明，KL散度的平方根下界是两个概率分布在L2范数（平方积分的平方根）上的差异。这个不等式在信息论和统计学中有广泛的应用，用于量化概率分布之间的距离。

Kullback-Leibler (KL) divergence

KL散度（Kullback-Leibler散度），也称为相对熵，是一种用于衡量两个概率分布之间差异的指标。给定两个概率分布 ($P$) 和 ($Q$)，KL散度的定义如下：

[ $D_{\text{KL}}(P\parallel Q)=\int P(x)\log \left(\frac{P(x)}{Q(x)}\right)dx$ ]

这个积分表示在样本空间上对 (P) 的每个事件的概率进行加权，权重是 ($P$) 对应事件的概率，然后乘以 ($P$) 和 ($Q$) 概率比的自然对数。

KL散度有一些重要的性质：

1. 非负性：($D_{\text{KL}}(P\parallel Q)\ge 0$)，等号成立当且仅当 ($P$) 和 ($Q$) 在所有点上都相等。
2. 不对称性：一般情况下，($D_{\text{KL}}(P\parallel Q)\ne D_{\text{KL}}(Q\parallel P)$)。它衡量了从 ($Q$) 到 ($P$) 的信息损失，和从 ($P$) 到 ($Q$) 的信息损失是不同的。
3. 不满足三角不等式：($D_{\text{KL}}(P\parallel R)\nleqq D_{\text{KL}}(P\parallel Q)+D_{\text{KL}}(Q\parallel R)$)。这意味着KL散度不满足三角不等式，因此不能被解释为标准的距离度量。

KL散度的应用广泛，包括在信息论、统计学、机器学习等领域，例如在变分推断、最大似然估计和生成模型中。

KL散度在matlab中的计算

KL（Kullback-Leibler）散度是衡量两个概率分布之间差异的一种方法。在Matlab中，你可以使用kldiv函数来计算两个概率分布的KL散度。这个函数通常包含在Statistics and Machine Learning Toolbox中，因此你需要确保你的Matlab版本中包含了这个工具箱。

以下是一个简单的示例，演示如何使用kldiv函数计算两个离散概率分布之间的KL散度：

% 定义两个离散概率分布
P = [0.3, 0.4, 0.3]; % 第一个分布
Q = [0.5, 0.2, 0.3]; % 第二个分布% 计算KL散度
kl_divergence = kldiv(P, Q);% 显示结果
disp(['KL散度：', num2str(kl_divergence)]);

请确保你的Matlab环境中已经安装了Statistics and Machine Learning Toolbox，以便使用kldiv函数。如果没有安装，你可以通过MathWorks官方网站获取该工具箱或者使用其他方法计算KL散度，例如手动实现KL散度的计算公式。

KL散度在隐蔽通信概率推导中的应用

Robust Beamfocusing for FDA-Aided Near-Field
Covert Communications With Uncertain Location
2023 IEEE ICC

Let $\left(D_{\mathrm{w}},{\theta }_{\mathrm{w}}\right)$ denote the location of Willie. We assume Willie is synchronized with Alice with the full knowledge of the carrier frequencies, and the channel vector ${\mathbf{h}}^H\left(D_{\mathrm{w}},{\theta }_{\mathrm{w}}\right)$ . This is the worst case for legitimate nodes to analyze the lower bound of covert communications performance. The hypothesis test at Willie is given by

$$\{\begin{array}{l}{\mathcal{H}}_0:y_{\mathrm{w}}^{(n)}=z_{\mathrm{w}}^{(n)},\\ {\mathcal{H}}_1:y_{\mathrm{w}}^{(n)}={\mathbf{h}}_{\mathrm{w}}^H\mathbf{w}{s}^{(n)}+z_{\mathrm{w}}^{(n)},\end{array}$$

where ${\mathbf{h}}_{\mathrm{w}}^H$ is short for ${\mathbf{h}}^H\left(D_{\mathrm{w}},{\theta }_{\mathrm{w}}\right)$ , and $z_{\mathrm{w}}^{(n)}\sim \mathcal{C}\mathcal{N}\left(0,{\sigma }_{\mathrm{w}}^2\right)$ is the AWGN at Willie with noise power ${\sigma }_{\mathrm{w}}^2$ . From (5), the probability distribution functions (PDFs) of ${\mathbf{y}}_{\mathrm{w}}={\left[y_{\mathrm{w}}^{(1)},y_{\mathrm{w}}^{(2)},\dots ,y_{\mathrm{w}}^{(N)}\right]}^T$ under ${\mathcal{H}}_0$ and ${\mathcal{H}}_1$ can be derived as

$${\mathbb{P}}_0\triangleq \mathbb{P}\left({\mathbf{y}}_{\mathrm{w}}\mid {\mathcal{H}}_0\right)=\frac{1}{{\pi }^N{\sigma }_{\mathrm{w}}^{2N}}{e}^{-\frac{{\mathbf{y}}_{\mathrm{w}}^H{\mathbf{y}}_{\mathrm{w}}}{{\sigma }_{\mathrm{w}}^2}}\text{\hspace{1em}(6)}$$

and：

$${\mathbb{P}}_1\triangleq \mathbb{P}\left({\mathbf{y}}_{\mathrm{w}}\mid {\mathcal{H}}_1\right)=\frac{1}{{\pi }^N{\left({\left|{\mathbf{h}}_{\mathrm{w}}^H\mathbf{w}\right|}^2+{\sigma }_{\mathrm{w}}^2\right)}^N}{e}^{-\frac{{\mathbf{y}}_{\mathrm{w}}^H{\mathbf{y}}_{\mathrm{w}}}{{|{\mathbf{h}}_{\mathrm{w}}^H|}^2+{\sigma }_{\mathrm{w}}^2}}\text{\hspace{1em}(7)}$$

respectively. Let ${\mathcal{D}}_0$ and ${\mathcal{D}}_1$ denote the decisions in favor of ${\mathcal{H}}_0$ and ${\mathcal{H}}_1$ , respectively. The false alarm and missed detection probabilities are defined as ${\mathbb{P}}_{FA}\triangleq \mathbb{P}\left({\mathcal{D}}_1\mid {\mathcal{H}}_0\right)$ and ${\mathbb{P}}_{MD}\triangleq \mathbb{P}\left({\mathcal{D}}_0\mid {\mathcal{H}}_1\right)$ , respectively. The detection performance of Willie is characterized by the sum of the detection error probabilities $\xi ={\mathbb{P}}_{FA}+{\mathbb{P}}_{MD}$ . Under the optimal detection, $\xi$ is minimized, which is denoted by ${\xi }^{\ast }$ . Then the covertness constraint of the system is expressed as ${\xi }^{\ast }\triangleq {\mathbb{P}}_{FA}+{\mathbb{P}}_{MD}\ge 1-ϵ$ , where
$ϵ\in [0,1]$ is an arbitrarily small positive constant indicating the level of covertness. Smaller \epsilon corresponds to stricter covertness requirement. Specially, when $ϵ=0$ , we have ${\xi }^{\ast }=1$ , which renders Willie’s detection to a blind guess. Moreover, according to Pinsker’s inequality [14], [15], we have ${\xi }^{\ast }\ge 1-\sqrt{\frac{\mathcal{D}\left({\mathbb{P}}_1\Vert {\mathbb{P}}_0\right)}{2}}$ , where $\mathcal{D}\left({\mathbb{P}}_1\Vert {\mathbb{P}}_0\right)={\int }_{\mathbf{y}}{\mathbb{P}}_1\log \frac{{\mathbb{P}}_1}{{\mathbb{P}}_0}\mathrm{d}\mathbf{y}$ is the Kullback-Leibler (KL) divergence of ${\mathbb{P}}_1$ and ${\mathbb{P}}_0$ . It can be easily verified that the original covertness constraint is satisfied as long as $\mathcal{D}\left({\mathbb{P}}_1\Vert {\mathbb{P}}_0\right)\le 2{ϵ}^2$ . Furthermore, by substituting (6) and (7) into the expression of $\mathcal{D}\left({\mathbb{P}}_1\Vert {\mathbb{P}}_0\right)$ , we have $\mathcal{D}\left({\mathbb{P}}_1\Vert {\mathbb{P}}_0\right)=N\zeta \left(\frac{{|{\mathbf{h}}_{\mathrm{w}}^H\mathbf{w}|}^2}{{\sigma }_{\mathrm{w}}^2}\right)$ , where $\zeta (x)=x-\log (1+x)$ for $x\ge 0$ is a monotonically increasing function w.r.t. $x$ . Then the original covertness constraint can be simplified by

$$\frac{{\left|{\mathbf{h}}_{\mathrm{w}}^H\mathbf{w}\right|}^2}{{\sigma }_{\mathrm{w}}^2}\le {\zeta }^{-1}\left(\frac{2{ϵ}^2}{N}\right)\text{\hspace{1em}(8)}$$

$\text{ where }{\zeta }^{-1}(x)\text{ is the inverse function of }\zeta (x)\text{. }$