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Dmitry Zaporozhets

Publications and source records attributed to Dmitry Zaporozhets.

At least 19 recordsLinked to original sources
arXiv2025

On Steiner entire function

We introduce and study the Steiner entire function, an analytic generating function for the intrinsic volumes of a convex compact set in a Hilbert space. This function extends the classical Steiner polynomial to infinite dimensions and encodes key geometric information about the set. We establish fundamental results on its order, type, and canonical product representation, and show how its analytic growth properties characterize Gaussian continuity. In particular, we provide new criteria for this property in terms of entire function theory, disprove a conjecture of Gao and Vitale, and conjecture a new characterization of admissible intrinsic volume sequences in infinite dimensions.

math.MG↗
arXiv2022

Intrinsic volumes of ellipsoids

We deduce explicit formulae for the intrinsic volumes of an ellipsoid in $\mathbb R^d$, $d\ge 2$, in terms of elliptic integrals. Namely, for an ellipsoid ${\mathcal E}\subset \mathbb R^d$ with semiaxes $a_1,\ldots, a_d$ we show that \begin{align*} V_k({\mathcal E})=κ_k\sum_{i=1}^da_i^2s_{k-1}(a_1^2,\dots,a_{i-1}^2,a_{i+1}^2,\dots,a_d^2)\int_0^{\infty}{t^{k-1}\over(a_i^2t^2+1)\prod_{j=1}^d\sqrt{a_j^2t^2+1}}\,\rm{d}t \end{align*} for all $k=1,\ldots,d$, where $s_{k-1}$ is the $(k-1)$-th elementary symmetric polynomial and $κ_k$ is the volume of the $k$-dimensional unit ball. Some examples of the intrinsic volumes $V_k$ with low and high $k$ are given where our formulae look particularly simple. As an application we derive new formulae for the expected $k$-dimensional volume of random $k$-simplex in an ellipsoid and random Gaussian $k$-simplex.

math.MG↗
arXiv2022

CGN: A Capacity-Guaranteed Network Architecture for Future Ultra-Dense Wireless Systems

The sixth generation (6G) era is envisioned to be a fully intelligent and autonomous era, with physical and digital lifestyles merged together. Future wireless network architectures should provide a solid support for such new lifestyles. A key problem thus arises that what kind of network architectures are suitable for 6G. In this paper, we propose a capacity-guaranteed network (CGN) architecture, which provides high capacity for wireless devices densely distributed everywhere, and ensures a superior scalability with low signaling overhead and computation complexity simultaneously. Our theorem proves that the essence of a CGN architecture is to decompose the whole network into non-overlapping clusters with equal cluster sum capacity. Simulation results reveal that in terms of the minimum cluster sum capacity, the proposed CGN can achieve at least 30% performance gain compared with existing base station clustering (BS-clustering) architectures. In addition, our theorem is sufficiently general and can be applied for networks with different distributions of BSs and users.

cs.IT↗
arXiv2022

Random section and random simplex inequality

Consider some convex body $K\subset\mathbb R^d$. Let $X_1,\dots, X_k$, where $k\leq d$, be random points independently and uniformly chosen in $K$, and let $ξ_k$ be a uniformly distributed random linear $k$-plane. We show that for $p\geq-d+k+1$, \[ \mathbb E\,|K\capξ_k|^{d+p}\leq c_{d,k,p} \cdot|K|^k\, \,\mathbb E\,|\mathrm{conv}(0,X_1, \dots,X_k)|^p, \] where $|\cdot|$ and $\mathrm{conv}$ denote the volume of correspondent dimension and the convex hull. The constant $c_{d,k,p}$ is such that for $k>1$ the equality holds if and only if $K$ is an ellipsoid centered at the origin, and for $k=1$ the inequality turns to equality. If $p=0$, then the inequality reduces to the Busemann intersection inequality, and if $k=d$ -- to the Busemann random simplex inequality. We also present an affine version of this inequality which similarly generalizes the Schneider inequality and the Blaschke-Grömer inequality.

math.MG↗
arXiv2021

What Should Future Wireless Network Architectures Be?

The accelerated convergence of digital and real-world lifestyles has imposed unprecedented demands on today's wireless network architectures, as it is highly desirable for such architectures to support wireless devices everywhere with high capacity and minimal signaling overhead. Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the future era. In this paper, we propose a capacity-centric (C$^2$) architecture for future wireless communication networks. It is designed based on the principles of maximizing the number of non-overlapping clusters with the average cluster capacity guaranteed to be higher than a certain threshold, and thus provides a flexible way to balance the capacity requirement against the signaling overhead. Our analytical results reveal that C$^2$ has superior generality, wherein both the cellular and the fully coordinated architectures can be viewed as its extreme cases. Simulation results show that the average capacity of C$^2$ is at least three times higher compared to that of the cellular architecture. More importantly, different from the widely adopted conventional wisdom that base-station distributions dominate architecture designs, we find that the C$^2$ architecture is independent of base-station distributions, and instead user-side information should be the focus in future wireless network architecture designs.

cs.IT↗
arXiv2021

Sharp inequalities for the mean distance of random points in convex bodies

For a convex body $K\subset\mathbb{R}^d$ the mean distance $Δ(K)=\mathbb{E}|X_1-X_2|$ is the expected Euclidean distance of two independent and uniformly distributed random points $X_1,X_2\in K$. Optimal lower and upper bounds for ratio between $Δ(K)$ and the first intrinsic volume $V_1(K)$ of $K$ (normalized mean width) are derived and degenerate extremal cases are discussed. The argument relies on Riesz's rearrangement inequality and the solution of an optimization problem for powers of concave functions. The relation with results known from the existing literature is reviewed in detail.

math.MG↗
arXiv2020

Convex hulls of several multidimensional Gaussian random walks

We derive explicit formulae for the expected volume and the expected number of facets of the convex hull of several multidimensional Gaussian random walks in terms of the Gaussian persistence probabilities. Special cases include the already known results about the convex hull of a single Gaussian random walk and the $d$-dimensional Gaussian polytope with or without the origin.

math.PR↗
arXiv2020

Angle sums of random polytopes

For two families of random polytopes we compute explicitly the expected sums of the conic intrinsic volumes and the Grassmann angles at all faces of any given dimension of the polytope under consideration. As special cases, we compute the expected sums of internal and external angles at all faces of any fixed dimension. The first family are the Gaussian polytopes defined as convex hulls of i.i.d. samples from a non-degenerate Gaussian distribution in $\mathbb R^d$. The second family are convex hulls of random walks with exchangeable increments satisfying certain mild general position assumption. The expected sums are expressed in terms of the angles of the regular simplices and the Stirling numbers, respectively. There are non-trivial analogies between these two settings. Further, we compute the angle sums for Gaussian projections of arbitrary polyhedral sets, of which the Gaussian polytopes are a special case. Also, we show that the expected Grassmann angle sums of a random polytope with a rotationally invariant law are invariant under affine transformations. Of independent interest may be also results on the faces of linear images of polyhedral sets. These results are well known but it seems that no detailed proofs can be found in the existing literature.

math.PR↗
arXiv2020

Random sections of spherical convex bodies

Let $K\subset\mathbb S^{d-1}$ be a convex spherical body. Denote by $Δ(K)$ the distance between two random points in $K$ and denote by $σ(K)$ the length of a random chord of $K$. We explicitly express the distribution of $Δ(K)$ via the distribution of $σ(K)$. From this we find the density of distribution of $Δ(K)$ when $K$ is a spherical cap.

math.PR↗
arXiv2020

Beta polytopes and Poisson polyhedra: $f$-vectors and angles

We study random polytopes of the form $[X_1,\ldots,X_n]$ defined as convex hulls of independent and identically distributed random points $X_1,\ldots,X_n$ in $\mathbb{R}^d$ with one of the following densities: $$ f_{d,β} (x) = c_{d,β} (1-\|x\|^2)^β, \qquad \|x\| < 1, \quad \text{(beta distribution, $β>-1$)} $$ or $$ \tilde f_{d,β} (x) = \tilde{c}_{d,β} (1+\|x\|^2)^{-β}, \qquad x\in\mathbb{R}^d, \quad \text{(beta' distribution, $β>d/2$)}. $$ This setting also includes the uniform distribution on the unit sphere and the standard normal distribution as limiting cases. We derive exact and asymptotic formulae for the expected number of $k$-faces of $[X_1,\ldots,X_n]$ for arbitrary $k\in\{0,1,\ldots,d-1\}$. We prove that for any such $k$ this expected number is strictly monotonically increasing with $n$. Also, we compute the expected internal and external angles of these polytopes at faces of every dimension and, more generally, the expected conic intrinsic volumes of their tangent cones. By passing to the large $n$ limit in the beta' case, we compute the expected $f$-vector of the convex hull of Poisson point processes with power-law intensity function. Using convex duality, we derive exact formulae for the expected number of $k$-faces of the zero cell for a class of isotropic Poisson hyperplane tessellations in $\mathbb R^d$. This family includes the zero cell of a classical stationary and isotropic Poisson hyperplane tessellation and the typical cell of a stationary Poisson--Voronoi tessellation as special cases. In addition, we prove precise limit theorems for this $f$-vector in the high-dimensional regime, as $d\to\infty$. Finally, we relate the $d$-dimensional beta and beta' distributions to the generalized Pareto distributions known in extreme-value theory.

math.PR↗
arXiv2020

Grassmann angles and absorption probabilities of Gaussian convex hulls

Let $M$ be an arbitrary subset in $\mathbb R^n$ with a conic (or positive) hull $C$. Consider its Gaussian image $AM$, where $A$ is a $k\times n$-matrix whose entries are independent standard Gaussian random variables. We show that the probability that the convex hull of $AM$ contains the origin in its interior coincides with the $k$-th Grassmann angle of $C$. Also, we prove that the expected Grassmann angles of $AC$ coincide with the corresponding Grassmann angles of $C$. Using the latter result, we show that the expected sum of $j$-th Grassmann angles at $\ell$-dimensional faces of a Gaussian simplex equals the analogous angle-sum for the regular simplex of the same dimension.

math.PR↗
arXiv2019

Absorption probabilities for Gaussian polytopes and regular spherical simplices

The Gaussian polytope $\mathcal P_{n,d}$ is the convex hull of $n$ independent standard normally distributed points in $\mathbb R^d$. We derive explicit expressions for the probability that $\mathcal P_{n,d}$ contains a fixed point $x\in\mathbb R^d$ as a function of the Euclidean norm of $x$, and the probability that $\mathcal P_{n,d}$ contains the point $σX$, where $σ\geq 0$ is constant and $X$ is a standard normal vector independent of $\mathcal P_{n,d}$. As a by-product, we also compute the expected number of $k$-faces and the expected volume of $\mathcal P_{n,d}$, thus recovering the results of Affentranger and Schneider [Discr. and Comput. Geometry, 1992] and Efron [Biometrika, 1965], respectively. All formulas are in terms of the volumes of regular spherical simplices, which, in turn, can be expressed through the standard normal distribution function $Φ(z)$ and its complex version $Φ(iz)$. The main tool used in the proofs is the conic version of the Crofton formula.

math.PR↗
arXiv2019

Joint distribution of conjugate algebraic numbers: a random polynomial approach

We count the algebraic numbers of fixed degree by their $\mathbf{w}$-weighted $l_p$-norm which generalizes the naïve height, the length, the Euclidean and the Bombieri norms. For non-negative integers $k,l$ such that $k+2l\leq n$ and a Borel subset $B\subset \mathbb{R}\times\mathbb{C}_+^l$ denote by $Φ_{p,\mathbf{w},k,l}(Q,B)$ the number of ordered $(k+l)$-tuples in $B$ of conjugate algebraic numbers of degree $n$ and $\mathbf{w}$-weighted $l_p$-norm at most $ Q$. We show that $$ \lim_{ Q\to\infty}\frac{Φ_{p,\mathbf{w},k,l}( Q,B)}{ Q^{n+1}}=\frac{\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})}{2ζ(n+1)}\int_B ρ_{p,\mathbf{w},k,l}(\mathbf{x},\mathbf{z}){\rm d}\mathbf{x}{\rm d}\mathbf{z}, $$ where ${\mathrm{Vol}}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})$ is the volume of the unit $\mathbf{w}$-weighted $l_p$-ball and $ρ_{p,\mathbf{w},k,l}$ will denote the correlation function of $k$ real and $l$ complex zeros of the random polynomial $\sum_{j=1}^n \frac{η_j}{w_j} z^j$, where $η_j $ are i.i.d. random variables with density $c_p e^{-|t|^p}$ for $0<p<\infty$ and with constant density on $[-1,1]$ for $p=\infty$. If the boundary of $B$ is of Lipschitz type, we also estimate the rate of convergence. We give an explicit formula for $ρ_{p,\mathbf{w},k,l}$, which in the case $k+2l=n$ has a very simple form. To this end, we obtain a general formula for the correlations between real and complex zeros of a random polynomial with arbitrary independent absolutely continuous coefficients.

math.NT↗
arXiv2019

Random affine simplexes

For a fixed $k\in\{1,\dots,d\}$ consider random vectors $X_0,\dots, X_{k}\in\mathbb R^d$ with an arbitrary spherically symmetric joint density function. Let $A$ be any non-singular $d\times d$ matrix. We show that the $k$-dimensional volume of the convex hull of affinely transformed $X_{i}$'s satisfies \[ |\mathrm{conv}(AX_0,\dots,AX_{k})|\stackrel{d}{=}\frac{|P_ξ\mathcal{E}|}{κ_k}\cdot|\mathrm{conv}(X_0,\dots,X_{k})|, \] where $\mathcal{E}:=\{\mathbf{x}\in\mathbb R^d:{\mathbf{x}^\top (A^\top A)^{-1}\mathbf{x}}\leq 1\}$ is an ellipsoid, $P_ξ$ denotes the orthogonal projection to a random uniformly chosen $k$-dimensional linear subspace $ξ$ independent of $X_0,\dots, X_{k}$, and $κ_k$ is the volume of the unit $k$-dimensional ball. We express $|P_ξ\mathcal{E}|$ in terms of Gaussian random matrices. The important special case $k=1$ corresponds to the distance between two random points: \[ |AX_0-AX_1|\stackrel{d}{=}\sqrt{\frac{λ_1^2N_1^2+\dots+λ_d^2N_d^2}{N_1^2+\dots+N_d^2}}\cdot|X_0-X_1|, \] where $N_1,\dots,N_d$ are i.i.d. standard Gaussian variables independent of $X_0,X_1$ and $λ_1,\dots,λ_d$ are the singular values of $A$. As an application, we derive the following integral geometry formula for ellipsoids: \[ \frac{κ_{d}^{k+1}}{κ_k^{d+1}}\,\frac{κ_{k(d+p)+k}}{κ_{k(d+p)+d}}\,\int\limits_{A_{d,k}}|\mathcal{E}\cap E|^{p+d+1}\,μ_{d,k}(dE)=|\mathcal{E}|^{k+1}\,\int\limits_{G_{d,k}}|P_L\mathcal{E}|^p\,ν_{d,k}(dL), \] where $p> -d+k-1$ and $A_{d,k}$ and $G_{d,k}$ are the affine and the linear Grassmannians equipped with their respective Haar measures. The case $p=0$ reduces to an affine version of the integral formula of Furstenberg and Tzkoni.

math.PR↗
arXiv2019

Angles of the Gaussian simplex

Consider a $d$-dimensional simplex whose vertices are random points chosen independently according to the standard Gaussian distribution on $\mathbb R^d$. We prove that the expected angle sum of this random simplex equals the angle sum of the regular simplex of the same dimension $d$.

math.PR↗
arXiv2018

Distribution of complex algebraic numbers on the unit circle

For $-π\leqβ_1<β_2\leqπ$ denote by $Φ_{β_1,β_2}(Q)$ the number of algebraic numbers on the unit circle with arguments in $[β_1,β_2]$ of degree $2m$ and with elliptic height at most $Q$. We show that \[ Φ_{β_1,β_2}(Q)=Q^{m+1}\int\limits_{β_1}^{β_2}{p(t)}\,{\rm d}t+O\left(Q^m\,\log Q\right),\quad Q\to\infty, \] where $p(t)$ coincides up to a constant factor with the density of the roots of some random trigonometric polynomial. This density is calculated explicitly using the Edelman--Kostlan formula.

math.NT↗
arXiv2017

Correlations between real conjugate algebraic numbers

For $B\subset\mathbb{R}^k$ denote by $Φ_k(Q;B)$ the number of ordered $k$-tuples in $B$ of real conjugate algebraic numbers of degree $\leq n$ and naive height $\leq Q$. We show that $$ Φ_k(Q;B) = \frac{(2Q)^{n+1}}{2ζ(n+1)} \int_{B} ρ_k(\mathbf{x})\,d\mathbf{x} + O\left(Q^n\right),\quad Q\to \infty, $$ where the function $ρ_k$ will be given explicitly. If $n=2$, then an additional factor $\log Q$ appears in the reminder term.

math.NT↗