## Research Topics

#### About wireless stochastic geometry

Stochastic geometry provides a natural way of defining and computing macroscopic properties of communication channels of multi-user information theory. These macroscopic properties are obtained by some averaging over all node patterns found in a large random network of the Euclidean plane or space.

For more on the field, see the wikipedia page on the matter.

This domain of research is currently expanding exponentially fast as shown by this curve communicated by Martin Haenggi.

#### Community Detection on Euclidean Random Graphs

In this paper, Abishek Sankararaman and François Baccelli introduce the problem of Community Detection on a new class of sparse spatial random graphs embedded in the Euclidean space. They consider the planted partition version of the random connection model graph studied in classical stochastic geometry. The nodes of the graph form a marked Poisson Point Process of intensity \lambda with each node being equipped with an i.i.d. uniform mark drawn from {-1,+1}. Conditional on the labels, edges are drawn independently at random depending both on the Euclidean distance between the nodes and the community labels on the nodes. The paper studies the Community Detection problem on this random graph which consists in estimating the partition of nodes into communities, based on an observation of the random graph along with the spatial location labels on nodes. For all dimensions greater than or equal to 2, they establish a phase transition in the intensity of the point process. They show that if the intensity is small, then no algorithm for Community Detection can beat a random guess. This is proven by introducing and analyzing a new problem called ‘Information Flow from Infinity’. On the positive side, they give an efficient algorithm to perform Community Detection as long as the intensity is sufficiently high. Along the way, a *distinguishability* result is established, which says that one can always infer the presence of a partition, even when one cannot identify the partition better than at random. This is a surprising new phenomenon not observed thus far in any non spatial Erdos-Renyi based planted partition models.

#### Control of queuing networks

One of the classical problems in queuing theory is to schedule customers/jobs in an optimal way. These problems are known as the scheduling problems. They arise in a wide variety of applications, in particular, whenever there are different customer classes present competing for the same resources. In a recent work “Ergodic control of multi-class M/M/N+M queues in the Halfin-Whitt regime”, Ari Arapostathis, Anup Biswas and Guodong Pang solved an ergodic control problem for multi-class many server queuing networks. The optimal solution of the queuing control problem can be approximated by that of the corresponding ergodic diffusion control problem in the limit. The proof technique introduces a new method of spatial truncation for the diffusion control problem.

#### Correlated shadowing in wireless stochastic geometry

Junse Lee, Xinchen Zhang and François Baccelli proposed new models for analyzing spatially correlated shadowing fields. These models allow one to analyze the interference field created by a wireless infrastructure through the walls and floors of a building with variable size rooms. These models provide a mathematical characterization of the interference distribution, which further leads to closed-from expressions for the coverage probability in cellular networks. Three network scenarios are studied: 2-D outdoor, 2-D indoor, and 3-D inbuilding.

#### Coverage in Cellular Networks Leveraging Vehicles

In this paper, Chang-sik Choi and François Baccelli analyzed an emerging architecture of cellular network utilizing both planar base stations uniformly distributed in Euclidean plane and base stations located on roads. An example of this architecture is that where, in addition to conventional planar cellular base stations and users, vehicles also play the role of both base stations and users. A Poisson line process is used to model the road network and, conditionally on the lines, linear Poisson point processes are used to model the vehicles on the roads. The conventional planar base stations and users are modeled by independent planar Poisson point processes. The joint stationarity of the elements in this model allows one to use Palm calculus to investigate statistical properties of such a network. Specifically, Chang-sik Choi and François Baccelli discussed two different Palm distributions, with respect to the user point processes depending on its type: planar or vehicular. They derived the distance to the nearest base station, the association of the typical users, and the coverage probability of the typical user in terms of integral formulas. Furthermore, this paper provides a comprehensive characterization of the performance of all possible cellular transmissions in this setting, namely vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), infrastructure-to-vehicle (I2V), and infrastructure-to-infrastructure (I2I) communications.

In the follow-up study, they analyzed the Voronoi tessellation with respect to the Poisson line Cox point process on the Euclidean plane. Using stochastic geometry, they discovered statistical properties of the Cox-Voronoi tessellation including its facet intensities and asymptotic shape. They proved that the typical Cox-Voronoi cell a.s. converges to the one-dimensional random segment.

#### Coverage in wireless networks

One of the most important geometric objects are the coverage regions of a transmitter or a set of transmitters. This question is jointly studied by Jeffrey Andrews, François Baccelli, Gustavo de Veciana, Robert Heath and Sanjay Shakkottai. Most of the initial steps are based on Poisson point processes. Lately, this continued with Yingzhe Li (Simons PhD student, ECE, UT Austin) to the case of determinantal point processes. Another line of work on studying cell-association problems in multi-technology cellular networks was carried out in this paper by Abishek Sankararaman, Jeong-woo Cho and François Baccelli.

#### Data driven discovery of sparse dynamics

Rachel Ward and collaborator Giang Tran (former Bing Instructor in the UT Mathematics department) are investigating the identification of a dynamical system (say, within the class of polynomial systems of ordinary differential equations) given snapshots of the system in time. Such problems prove challenging when there is a high level of noise on the data. In the paper Exact recovery of Chaotic systems from highly corrupted data, they show that if the underlying trajectory exhibits ergodicy or chaos, and if the underlying dynamics have a sparse representation with respect to the polynomial basis, then a LASSO / l1 type algorithm will exactly recover the underlying dynamics even when most of the data is highly corrupted. This establishes a new link between the areas of dynamical systems and machine learning / sparse recovery, and many interesting questions remain.

#### Detecting planted communities in random graphs

The stochastic block model (aka. planted partition model) is a popular model for representing networks with communities. Elchanan Mossel, Joe Neeman, and Allan Sly have been investigating algorithms and fundamental limits for detecting and recovering these communities. They established sharp transitions for the problem of extracting non-trivial information and the problem of exactly recovering communities. They also gave a new algorithm that obtains provably optimal accuracy for the problem of detecting communities in “Consistency thresholds for the planted bisection model” and “Belief propagation, robust reconstruction, and optimal recovery of block models“.

#### Dimensions of unimodular random discrete spaces

This work by F.Baccelli, M.-O. Haji-Mirsadeghi, and A. Khezeli, is focused on large scale properties of infinite graphs and discrete subsets of the Euclidean space. It presents two new notions of dimension, namely the unimodular Minkowski and Hausdorff dimensions, which are inspired by the classical Minkowski and Hausdorff dimensions. These dimensions are defined for unimodular discrete spaces, which are defined in this work as a class of random discrete metric spaces with a distinguished point called the origin. These spaces provide a common generalization to stationary point processes under their Palm version and unimodular random rooted graphs.

The main novelty is the use of unimodularity in the definitions where it suggests replacing the infinite sums pertaining to coverings by large balls by the expectation of certain random variables at the origin. In addition, the main manifestation of unimodularity, that is the mass transport principle, is the key element in the proofs and dimension evaluations.

These dimensions are connected to the growth rate of balls. In particular, versions of the mass distribution principle, Billingsley’s lemma, and Frostman’s lemma are established for unimodular discrete spaces.

The dimensions in question are explicitly evaluated or conjectured for a comprehensive set of examples pertaining to the theory of point processes, unimodular random graphs, and self-similarity.

On the Dimension of Unimodular Discrete Spaces, Part I: Definitions and Basic Properties

On the Dimension of Unimodular Discrete Spaces, Part II: Relations with Growth Rate

#### Dynamic games with asymmetric information

In the paper, Deepanshu Vasal considered a general finite-horizon non-zero-sum dynamic game with asymmetric information with N selfish players, where there exists an underlying state of the system that is a controlled Markov process, controlled by players’ actions. In each period, a player makes both private and common observations about the state of the system. An appropriate notion of equilibrium for such processes includes Perfect Bayesian equilibrium (PBE) which consists of a strategy and a belief profile of the players. Such equilibrium strategies and beliefs are coupled together across time and thus computing equilibria for such games is equivalent to solving a fixed-point equation which grows double exponentially with time, rendering such problems intractable. In this paper, a sequential decomposition methodology is presented to compute *structured* perfect Bayesian equilibria (SPBE) of this game. In general, these equilibria exhibit signaling behavior, i.e. players’ actions reveal part of their private information that is payoff relevant to other users.

#### Entropy of point processes

François Baccelli and Jae Oh Woo initiated a study on the entropy and mutual information of point processes (On the Entropy and Mutual Information of Point Processes). The main new mathematical objects are the relative entropy rate and the mutual information rate of two stationary point processes. They also derived expression of the mutual information rate in the case of a homogeneous point process and its displacement. This machinery is used to revisit the Gaussian noise channel in the Shannon-Poltyrev regime recently introduced in Capacity and error exponents of stationary point processes under random additive displacements.

#### Extremes of spatial shot noise processes

Anup Biswas and François Baccelli studied the scaling limit of a class of shot-noise fields defined on an independently marked stationary Poisson point process and with a power law response function. Under appropriate conditions, they showed that the shot-noise field can be scaled suitably to have a non degenerate alpha-stable limit, as the intensity of the underlying point process goes to infinity. More precisely, finite dimensional distributions converge and the finite dimensional distributions of the limiting random field have i.i.d. stable random components. This limit is hence called the alpha- stable white noise field. Analogous results are also obtained for the extremal shot-noise field which converges to a Fréchet white noise field.

#### Incentive Compatible Mechanisms

Bo Lin and Ngoc Mai Tran applied results in tropical geometry to the study of mechanism design (Two-player incentive compatible mechanisms are affine maximizers). They proved that for two-player games on a discrete type space, any given mechanism can be turned into an affine maximizer through a nontrivial perturbation of the type space. In their proof, they connected the incentive compatible mechanisms of a type space T to the tropical determinant of the minors of the matrix T.

#### Information theory and high dimensional stochastic geometry

The most basic capacity and error exponent questions of information theory can be expressed in terms of random geometric objects living in Euclidean spaces with dimensions tending to infinity. This approach was introduced by Venkat Anantharam and François Baccelli to evaluate random coding error exponents in channels with additive stationary and ergodic noise. More generally, the analysis of stochastic geometry in the Shannon regime leads to new high dimension stochastic geometry questions that are currently investigated. Eliza O’Reilly and and Francois Baccelli have also studied determinantal point processes in high dimensions. This work describes the strength and reach of repulsion of a typical point of certain parametric families of determinantal point process in the Shannon regime.

#### Mathematical problems in neuroscience

Recent advances in neuroscience provide theoretical neuroscientists with a vast wealth of new data and open questions related to information theory, high-dimensional geometry of representation and computation, and dynamics in the brain. The groups of Ila Fiete, Ngoc Mai Tran and Thibaud Taillefumier study these questions from analytical and numerical perspectives. Fiete and Tran have recently studied the learning capacity of neural networks (see “A binary Hopfield network with 1/\log(n) information rate and applications to grid cell decoding“, “ Robust exponential memory in Hopfield networks“, and “ Associative content-addressable networks with exponentially many robust stable states“).

#### Metastability of queuing networks with mobile servers

In a paper entitled Metastability of Queuing Networks with Mobile Servers, A. Rybko, S. Shlosman, A. Vladimorov and F. Baccelli study symmetric queuing networks with moving servers and FIFO service discipline. The mean-field limit dynamics demonstrates unexpected behavior which is attributed to the meta-stability phenomenon. Large enough finite symmetric networks on regular graphs are proved to be transient for arbitrarily small inflow rates. However, the limiting non-linear Markov process possesses at least two stationary solutions. The mean-field analysis is based on the Non Linear Markov Process developed for this type of queuing networks in Queuing Networks with Varying Topology – A Mean-Field Approach.

#### Moving user time series in SNR stochastic geometry

With Pranav Madadi, F. Baccelli, and G. de Veciana analyzed the temporal variations in the Shannon rate experienced by a user moving along a straight line in a cellular network represented by a Poisson-Voronoi tessellation. We consider a network that is shared by static users distributed as a Poisson point process and analyzed the time series of the final shared rate and the number of users sharing the network. The paper On Shared Rate Time Series for Mobile Users in Poisson Networks was focused on the noise limited case.

#### On the steady state of continuous time stochastic opinion dynamics

François Baccelli, Sriram Vishwanath and Jae Oh Woo proposed a computational framework for continuous time opinion dynamics with additive noise. They derived a non-local partial differential equation for the distribution of opinions differences. They used Mellin transforms to solve the stationary solution of this equation in closed form. This approach can be applied both to linear dynamics on an interaction graph and to bounded confidence dynamics in the Euclidean space. To the best of our knowledge, the closed form expression on the stationary distribution of the bounded confidence model is the first quantitative result on the equilibria of this class of models.

#### Optimization of DNA sequencing

High throughput DNA sequencing technology has greatly increased the speed and reduced the cost of genome sequencing. The process is divided into to two steps: generating a library of short reads and reassembling those reads into the original genome. Eliza O’Reilly, François Baccelli, Gustavo de Veciana, and Haris Vikalo have worked on modeling this process using stochastic geometry and queueing theory in order to optimize the output of correct reads and the probability of successful reassembly (see End-to-End Optimization of High Throughput DNA Sequencing).

#### Order parameters of dense phases

Dense phases of emergent systems, such as constrained complex networks,

exhibit distinct characteristics, the most studied being broken symmetry.

However for practical purposes “rigidity”, the resistance to change, is

also of wide interest. There are difficulties analyzing rigidity since

when perturbed a system can easily move out of its phase. A new approach to

overcome this contradiction has been initiated by David Aristoff and

Charles Radin: see the discussion in Quanta Magazine. Another characteristic

of dense phases are their nonspherical `Wulff’ shapes, polyhedral for ordinary crystals.

This is examined in this expository paper by Charles Radin, and in a related direction in this paper by Charles Radin, Kui Ren, and Lorenzo Sadun.

In a different direction, the process of nucleation is a dynamical signature of the creation of a dense phase, which can appear even in systems far removed from ordinary atomic materials, as shown in this paper by Frank Rietz, Charles Radin, Harry Swinney and Matthias Schroeter and follow-up paper by Charles Radin and Harry Swinney. All the above characteristics make essential use of finite systems, a nonstandard approach to understanding emergent phases.

#### Phases and phase transitions in complex networks

In the phenomenon of emergence, a system of many interacting objects

exhibits the collective behavior of one or more “phases”, which are

only detectable or even meaningful for the system as a whole. This is

an organizing principle widely used in biology, physics and indeed all

the sciences: crystals, hurricanes, animal flocking etc. One wants to

understand the spontaneous appearance of phases in systems of large

size, in particular to determine a mechanism of some generality. A

convenient framework for such an analysis is large networks with

constraints. Such an analysis has been undertaken by the group of

Richard Kenyon, Charles Radin, Kui Ren and Lorenzo Sadun, on entropy singularities, the edge/triangle system I, the edge/triangle system II, multipodal structure,

order-disorder transitions and oversaturated networks. There is also a related asymptotic analysis of large permutations undertaken by the group of Richard Kenyon, Daniel Kral, Charles Radin and Peter Winkler: permutations with fixed pattern densities, and a review of phases in general combinatorial systems.

#### Point maps on point processes

A compatible point-shift f maps, in a translation invariant way, each point of a stationary point process N to some point of N. It is fully determined by its associated point-map, g, which gives the image of the origin by f. The initial question studied by Mir-Omid Haji-Mirsadeghi and François Baccelli is whether there exist probability measures which are left invariant by the translation of -g. The point map probabilities of N are defined from the action of the semigroup of point-map translations on the space of Palm probabilities, and more precisely from the compactification of the orbits of this semigroup action. If the point-map probability is uniquely defined, and if it satisfies certain continuity properties, it then provides a solution to the initial question. Point-map probabilities are shown to be a strict generalization of Palm probabilities: when the considered point-shift f is bijective, the point-map probability of N boils down to the Palm probability of N. When it is not bijective, there exist cases where the point-map probability of N is absolutely continuous with respect to its Palm probability, but there also exist cases where it is singular with respect to the latter.

Each such point-shift defines a random graph on the points of the point process. The connected components of this graph can be split into a collection of foils, which are the analogue of the stable manifold of the point-shift dynamics.

The same authors give a general classification of point-shifts in terms of the cardinality of the foils of these connected components. There are three types: F/F, I/F and I/I as shown in the paper Point-Shift Foliation of a Point Process.

#### Point processes on topological groups

Using the framework of Günter Last, James Murphy has extended the cardinality classification to the case of point processes on unimodular groups. J. Murphy has studied point-shifts of point processes on topological groups at length.

#### Poisson Hail

Consider a queue where the server is the Euclidean space, the customers are random closed sets (RACS) of the Euclidean space. These RACS arrive according to a Poisson rain and each of them has a random service time (in the case of hail falling on the Euclidean plane, this is the height of the hailstone, whereas the RACS is its footprint). The Euclidean space serves customers at speed 1. The service discipline is a hard exclusion rule: no two intersecting RACS can be served simultaneously and service is in the First In First Out order (only the hailstones in contact with the ground melt at speed 1, whereas the other ones are queued; a tagged RACS waits until all RACS arrived before it and intersecting it have fully melted before starting its own melting). We prove that this queue is stable for a sufficiently small arrival intensity, provided the typical diameter of the RACS and the typical service time have finite exponential moments.

In Shape Theorems For Poisson Hail on a Bivariate Ground, H. Chang, S. Foss and F. Baccelli have extended this Poisson Hail model to the situation where the service speed is either zero or infinity at each point of the Euclidean space. Tools pertaining to sub-additive ergodic theory are used to establish shape theorems for the growth of the ice-heap under light tail assumptions on the hailstone characteristics. The asymptotic shape depends on the statistics of the hailstones, the intensity of the underlying Poisson point process and on the geometrical properties of the zero speed set.

#### Preprint of a new book on point processes and stochastic geometry

This book is centered on the mathematical analysis of random structures embedded in the Euclidean space or more general topological spaces, with a main focus on random measures, point processes, and stochastic geometry. Such random structures have been known to play a key role in several branches of natural sciences (cosmology, ecology, cell biology) and engineering (material sciences, networks) for several decades. Their use is currently expanding to new fields like data sciences. The book was designed to help researchers finding a direct path from the basic definitions and properties of these mathematical objects to their use in new and concrete stochastic models. The theory part of the book is structured to be self-contained, with all proofs included, in particular on measurability questions, and at the same time comprehensive. In addition to the illustrative examples which one finds in all classical mathematical books, the document features sections on more elaborate examples which are referred to as models}in the book. Special care is taken to express these models, which stem from the natural sciences and engineering domains listed above, in clear and self-contained mathematical terms. This continuum from a comprehensive treatise on the theory of point processes and stochastic geometry to the collection of models that illustrate its representation power is probably the main originality of this book. The book contains two types of mathematical results: (1) structural results on stationary random measures and stochastic geometry objects, which do not rely on any parametric assumptions; (2) more computational results on the most important parametric classes of point processes, in particular Poisson or Determinantal point processes. These two types are used to structure the book. The material is organized as follows. Random measures and point processes are presented first, whereas stochastic geometry is discussed at the end of the book. For point processes and random measures, parametric models are discussed before non-parametric ones. For the stochastic geometry part, the objects as point processes are often considered in the space of random sets of the Euclidean space. Both general processes are discussed as, e.g., particle processes, and parametric ones like, e.g., Poisson Boolean models of Poisson hyperplane processes. We assume that the reader is acquainted with the basic results on measure and probability theories. We prove all technical auxiliary results when they are not easily available in the literature or when existing proofs appeared to us not sufficiently explicit. In all cases, the corresponding references will always be given.

Here is the pdf file of the preprint: BBK

#### Scaling laws for ergodic spectral efficiency in MIMO Poisson networks

Ergodic spectral efficiency quantifies the achievable Shannon transmission rate per unit area, and captures the effects of rate adaptation techniques. Junse Lee, Namyoon Lee and François Baccelli studied the benefits of multiple antenna communication in ad-hoc networks using this metric. In this work, the primary finding is that, with knowledge of channel state information between a receiver and its associated transmitter, the ergodic spectral efficiency can be made to scale linearly with both 1) the minimum of the number of transmit and receive antennas and 2) the density of nodes. This scaling law is achieved when the multiple transmit antennas send multiple data streams and the multiple receive antennas are leveraged to cancel interference. Spatial multiplexing transmission methods are shown to be essential for obtaining better and eventually optimal scaling laws in such random wireless networks.

#### Spatial birth and death processes

Spatial point processes involving birth and death dynamics are ubiquitous in networks. Such dynamics are particularly important in peer-to-peer networks and in wireless networks. In the paper “Mutual Service Processes in R^d, Existence and Ergodicity”, Fabien Mathieu (Bell Laboratories), Ilkka Norros (VTT) and François Baccelli proposed a way to analyze the long term behavior of such dynamics on Euclidean spaces using coupling techniques. This line of though is continued by Mayank Manjrekar on other classes of processes like hard core spatial birth and death processes.

#### Spatial Birth-Death Wireless Networks

In this paper, Abishek Sankararaman and François Baccelli introduce a new form of spatial dynamics motivated by ad-hoc wireless networks. They study a birth death process where particles arrive in space as a Poisson process in space and time, and depart the system on completion of file transfer. The instantaneous rate of file transfer of any link is given by the Shannon formula of treating interference as noise. As the instantaneous interference seen by a link is dependent on the configuration of links present, this dynamics is an example of one where dynamics shapes geometry and in turn the geometry shapes the dynamics. In this paper, the authors establish a sharp phase-transition for stability of such dynamics. Moreover, whenever such dynamics is stable, they prove that the steady state is a clustered point process. Through simulations, they also argue that such dynamics cannot be simplified to any form of non-spatial queuing type dynamics. Lately, this paper with Sergey Foss extended the dynamics on grids to show a similar stability phase-transition in the case of infinite network, i.e. in an infinite grid.

#### Stochastic opinion dynamics in social networks

Avhishek Chatterjee, François Baccelli and Sriram Vishwanath proposed a stochastic extension of the bounded confidence model where opinions take their values in the Euclidean space and where friendship and interactions are dynamically defined through time varying and random neighborhoods. Two basic sub-models are defined: the influencing model where each agent is an attractor to the opinions of its neighbors and the listening model where each agent gathers information from others to update its own opinions. The general model contains a rich set of variants for which they proposed a classification. They analyzed the stability of its dynamics. The analysis highlights the need of certain leaders with heavy tailed neighborhoods for stability to hold. See Pairwise Stochastic Bounded Confidence Opinion Dynamics: Heavy Tails and Stability

#### Synchrony in stochastic spiking neural networks

Neural systems propagate information via neuronal networks that transform sensory input into distributed spiking patterns, and dynamically process these patterns to generate behaviorally relevant responses. The presence of noise at every stage of neural processing imposes serious limitation on the coding strategies of these networks. In particular, coding information via spike timings, which presumably achieves the highest information transmission rate, requires neural assemblies to exhibit high level of synchrony. Thibaud Taillefumier and collaborators are interested in understanding how synchronous activity emerges in modeled populations of spiking neurons, focusing on the interplay between driving inputs and network structure. Their approach relies on methods from Markov chain, point processes, and diffusion processes theories, in combination with exact event-driven simulation techniques. The ultimate goal is two-fold: 1) to identify the input/structure relations that optimize information transmission capabilities and 2) to characterize the “physical signature’’ of such putative optimal tunings in recorded spiking activity.

#### Vertex Shifts on Random Graphs

Ali Khezeli, Mir-Omid Haji-Mirsadeghi and François Baccelli studied dynamics on the vertices of a random graph in the paper Dynamics on Unimodular Graphs. The first result of the paper is a classification of vertex-shifts on unimodular random networks. Each such vertex-shift partitions the vertices into a collection of connected components and foils, as in the case of point-shifts on point processes.

The classification is based on the cardinality of the connected components and foils. Up to an event of zero probability, there are three types of foliations in a connected component: F/F (with finitely many finite foils), I/F (infinitely many finite foils), and I/I (infinitely many infinite foils).

An infinite connected component of the graph of a vertex-shift on a random network forms an infinite directed tree with one selected end which is referred to as an Eternal Family Tree. An Eternal Family Tree contains a subtree which is a stochastic generalization of a branching process. In a unimodular Eternal Family Tree, the subtree in question is a generalization of a critical branching process. In a $\sigma$-invariant Eternal Family Tree, the subtree is a generalizisation of a non-necessarily critical branching process. The latter trees allow one to analyze dynamics on networks which are not necessarily unimodular.

#### Zeros of Random Tropical Polynomials

Ngoc Mai Tran and François Baccelli derived a tropical version of the result of Kac on the zeros of polynomials with random coefficients (Zeros of Random Tropical Polynomials, Random Polygons and Stick-Breaking).

The common roots of tropical of a class of random polynomials in two variables is analyzed in a recent work by the same authors in Iterated Gilbert Mosaics and Poisson Tropical Plane Curves using a stochastic geometry approach based on iterated random tessellations.