Point process definition and notation

A non-random collection of points located on some space is called a point pattern in spatial statistics. Informally, you can interpret a point process as a collection of random points scattered over some underlying mathematical space, meaning each outcome or realization of a point process forms a point pattern. Using such intuition gets you pretty far. But if we want to be more mathematically formal, we need to use precise mathematical objects.

Historically, there’s a couple main interpretations of a point process, which is also called a random point field. The different interpretations partly explain the various terminology and notation used in the theory of point processes, but now a standard mathematical approach is used.

In this post I will cover the main definitions, terminology and notation used in the theory and application of point processes. I won’t delve too much into the precise details, giving just an outline with a references at the end.

Underlying mathematical space

We consider a point process defined on some underlying mathematical space \(\mathbb{S}\), which is sometimes called the carrier space or state space.  We further assume that the space is measurable by having a Borel \(\sigma\)-algebra \(\mathcal{S}\).

In practice, the underlying space is usually the real line \(\mathbb{R}\), the plane \(\mathbb{R}^2\), or some other familiar mathematical space like a square lattice. More generally, a point process can be defined on any metric space, allowing for the notion of distance. Mathematicians study point processes in even more general settings by defining them on, for example, a locally compact second countable Hausdorff space, but such generality is not needed for most people and their applications.

Modern probability approach

In modern probability theory, if we want to define a random mathematical object, we start with a probability space or triple \((\Omega,\mathcal{A},\mathbb{P})\), where:

  1. \(\Omega\) is a sample space, which is a set of outcomes;
  2. \(\mathcal{A}\) is a \(\sigma\)-algebra or \(\sigma\)-field, which is a family of events;
  3. \(\mathbb{P}\) is a probability measure, which assigns probability to each event in \(\mathcal{A}\).

To gain some intuition, David Williams says to imagine that Tyche, Goddess of Chance, chooses a point \(\omega\in\Omega\) at random according to the law \(\mathbb{P}\) such that an event \(A\in \mathcal{A}\) has a probability given by \(\mathbb{P}(A)\), where we understand probability with our own intuition. (We can also choose \(\omega\in\Omega\) by using some physical experiment, as long as it is truly random.)

Now we can define random objects by using a certain measurable function or mapping that maps to a suitable space of objects. For example, a real-valued random variable is a measurable function from \(\Omega\) to the real line; a random matrix is a measurable function from \(\Omega\) to some space of matrices; and, as John Kingman quips, a random elephant is just a measurable function from \(\Omega\) to some suitable space of elephants.

But what space should we use for a point process? To answer that, we need to interpret a point process as a suitable mathematical object.

Interpretation

There are different ways to interpret a point process, which is often denoted by a single letter, for example, \(N\) or \(\Phi\). (The convention of using the Greek letter \(\Phi\) comes from German mathematicians, but some prefer not to use \(\Phi\), as it’s often used for the normal cumulative distribution function.) If the point process is defined on a space like the real like, where the points can be ordered, then additional interpretations exist, but mathematicians assume the order of the points does not matter, limiting the possible interpretations.

Random closed set

In mathematics a collection of distinct things is formalized by a mathematical object called a set. We say that a set contains elements or members, and a set never contains more than one of the same element. Sets are fundamental objects with set concepts and notation being found everywhere in mathematics.

We now define a common type of point process, which we can formalize with the concept of a set.

A point process is simple if the probability of all points of the point process being distinct is one.

In other words, for a simple point process, there is zero probability of two or more of its points being found in the same location of the underlying state space \(\mathbb{S}\), which brings us to our first interpretation.

A simple point process can be interpreted as a random closed set.

More specifically, we can interpret a simple point process as a (measurable) mapping from a sample space \(\Omega\) to the space of closed sets \(\mathbb{F}\), meaning that each realization of a simple point process is a closed set \(\phi\in \mathbb{F}\).

Point process theory has adopted the notation from set theory. For example, if we want to say some point, which we denote by \(x\), of the underlying space \(\mathbb{S}\) belongs to or is a member of a simple point process \(\Phi\), then we can simply write \(x\in \Phi\). We can also write a point process as \(\{x\}_i\) to highlight its interpretation as a random closed set of points.

The theory of random sets, which is a field of study in its own right, can be applied to point processes owing to this interpretation. But for non-simple point processes, we need another point process interpretation.

Random measures

Modern integration theory is based on measure theory, which revolves around the concept of a set function known as a measure. In addition to a couple of other properties, when you apply this function to a set, it gives a number, such as a integer or real number. For example, a counting measure gives you the number elements in a set, which could be a subspace \(B\), such as a region of the plane \(B \subset \mathbb{R}^2\). (The letter \(B\) is often used for sets in measure and probability theory as it’s typically assumed that the sets are Borel sets, which form a very large family of well-behaved sets in terms of measurability.) The concept of a counting measure gives us the second and now standard interpretation of a point process.

A point process can be interpreted a random counting measure.

More specifically, we define a point process as a mapping from a sample space \(\Omega\) to the space of counting measures \(\mathbb{M}\), meaning that each realization of a point process is a counting measure \(\phi\in \mathbb{M}\). Some mathematicians even say a point process is just another name for a random counting measure. The techniques of random measure theory provide alternative (and arguably main) approach to study point processes.

This standard interpretation of a point process means that point process theory borrows heavily notation from measure theory and calculus. For example, in measure theory we can write a (non-random) counting measure as \(\#\), so \(\#(B)=n\) is how we write that the set \(B\) contains \(n\) points. We can then write the the number of points of a point process \(\Phi\) located in some (Borel) set \(B\) as \(N(B) =\#( B \cap \Phi)\), where \(N(B)\) is a random variable. In this expression, the point process is denoted by \(\Phi\), while\(N(B)\) is the number of points of \(\Phi\) in \(B\), meaning \(N\) is a random counting measure .

Notation

The main interpretations of point processes as random sets and counting measures is captured with the notation:

  • \(\Phi\) is a set of random points.
  • \(\Phi(B)\) is a random variable that gives the number of points of \(\Phi\) located in the (Borel) set \(B\).

This is the notation often used in point process theory. It implies
$$
\Phi(B) =\#(B \cap \Phi).
$$

We now look at how this notation is used in point process theory.

Sums

If \(f\) is some (measurable) function on the underlying space \(\mathbb{S}\), such as Euclidean space \(\mathbb{R}^d\), then we can write the sum of \(f(x)\) over all the points of a simple point process \(\Phi\) as
$$
\sum_{x\in \Phi}f(x)\,,
$$
where we are using the random set interpretation.

For any point process \(\Phi\), we can also write the sum as
$$
\int_{\mathbb{S}} f(x) \Phi(dx) \,,
$$
which highlights the interpretation of the point process \(\Phi\) as a random counting measure. Of course, we can use different integral notation, giving, for example, the expression
$$
\int_{\mathbb{S}} f d\Phi \,,
$$
which denotes the same sum.

We can illustrate the dual interpretation of a point process by writing the number of point of a simple point process \(\Phi\) existing in a set \(B\) as
$$
\Phi(B)= \sum_{x\in \Phi}1_B(x)\,,
$$
where the indicator function \(1_B(x) =1\) if the point \(x\) is exists in the set \(B\), and \(1_B(x) =0\) otherwise. In this setting, \(1_B(x)\) is also known as a Dirac measure, as it gives a measure of the set \(B\). We can see in this expression that the random measure interpretation is on the left-hand side, while the random set notation is on the right-hand side.

Expectations

We can write the average or expected value of a sum of functions over a simple point process \(\Phi\) as
$$
\mathbb{E}\left[\sum_{x\in \Phi}f(x)\right] \,,
$$
or for any point process \(\Phi\) as
$$
\int_{\textbf{N}}\sum_{x\in \Phi}f(x) \mathbb{P}(d\Phi)\,,
$$
where \(\mathbb{P}\) is an appropriate probability measure defined on the space of counting functions \(\textbf{N}\), thus illustrating the random measure interpretation.

We can write the expected value of \(\Phi(B)\), which is the definition of the intensity measure of a point process \(\Phi\), as
$$
\mathbb{E}[\Phi(B)]=\mathbb{E}\left( \sum_{x\in \Phi}1_B(x)\right) \qquad \text{or} \qquad \mathbb{E}[\Phi(B)]=\int_{\textbf{N}}\sum_{x\in \Phi}1_B(x) P(d\Phi).
$$
which is also known as the mean measure or first moment measure of \(\Phi\).

Events

In probability we want to describe the behaviour of certain events, such as flipping at last three heads across ten coin flips. For point processes, events are simply configurations with a certain (geometric) property, such as no points existing in a certain region or all the points being a fixed minium distance from each other.

Typically, when being mathematically abstract, we denote an event with a single letter, such as \(\Gamma\). Then to denote that a point process satisfies this condition we write \(\Phi\in \Gamma\). In other words, the point process \(\Phi\) has the property \(\Gamma\). We can then write the probability of the event (or configuration) \(\Gamma\) of occurring as
$$
\mathbb{P}(\Gamma)= \mathbb{P}(\Phi\in \Gamma ) \,.
$$

Uppercase and subscript notation

The convention in probability is usually to denote random objects, such as random variables and point processes, with uppercase (or capital) letters. Conversely, a non-random object, such as the realization of a random variable or point process is denoted by a lowercase letter. For example, \(\Phi\) is a point processes, while \(\phi\) is a point pattern, which may be a realization of the point process \(\Phi\).

With this convention, we can denote an arbitrary point process of a point process \(\Phi\) by \(X\), meaning \(X\in \Phi\). (But such a point is also a point on the underlying non-random space \(\mathbb{S}\) on which the point process \(\Phi\) is defined.) We also see lowercase used for the point, giving \(x\in \Phi\).

Sometimes subscripts are used to emphasize some type of numbering of points, giving, for example, two points \(X_1\in \Phi\) and \(X_2\in \Phi\). Sometimes authors will write something like

$$
\sum_{X_i\in \Phi}f(X_i)\,,
$$

but this redundant notation as \(X_i\) is a dummy variable, so you can omit the subscript in such an expression.

I’ve also seen papers, where the letter with and without a subscript denotes, respectively, the point process and a point belonging to the point process, giving, for example, \(X=\{ X_i\}_i\) and \(X_i\in X\).

Important concepts

In the theory point processes, like any other field of mathematics, there are various important concepts for understanding and proving various results. Without going in the details, these include Campbell’s theorem, Laplace functional, Palm calculus, void probability, and factorial moment measures. In future posts, I’ll detail some of these concepts.

Further reading

Wikipedia is a good start place to start, particularly the articles on point processes and point process notation, though the former is too mathematical for a Wikipedia article. The standard reference on point processes was the An Introduction to the Theory of Point Processes by Daley and Vere-Jones, which now spread across volume one and two, but I would not learn the subject with these books. The classic text Stochastic Geometry and its Applications by Chiu, Stoyan, Kendall and Meck covers point processes and the varying notation in Chapters 2 and 4. The similar material is covered in the previous edition by Stoyan, Kendall and Mecke. A more mathematical book that covers point processes and random sets is Stochastic and integral geometry by Schneider and Weil. Point processes are also covered in a recent readable tutorial on Palm calculus and Gibbs point processes, which will be the subject of another post.

Spatial statistics builds of point process theory, giving good texts for learning the basics of point processes. I suggest the lectures notes by Baddeley, which form Chapter 1 of these published lectures, edited by Baddeley, Bárány, Schneider, and Weil. I always recommend the book Spatial Point Patterns: Methodology and Applications with R written by spatial statistics experts Baddeley, Rubak and Turner, which covers the spatial statistics (and point process simulation) R-package spatstat. Another good book is Statistical Inference and Simulation for Spatial Point Processes by Møller and Waagepetersen, but there are many more.

In recent years, point process theory (under the guise of stochastic geometry) has been used to model wireless networks. An early treatment of the subject is the two-volume textbook Stochastic Geometry and Wireless Networks by Baccelli and Błaszczyszyn, where the first volume is on theory and the second volume is on applications. Haenggi wrote a very readable introductory book called Stochastic Geometry for Wireless networks.

For the more mathematically brave, there’s the recent book Random Measures, Theory and Applications by Kallenberg who is the authority of the subject, having written earlier books, but these have now become obsolete with this recent publication. Another mathematically challenging book is The Theory of Random Sets by Molchanov, but it has less emphasis on point processes.

A very recent book (manuscript) is Random Measures, Point Processes, and Stochastic Geometry by Baccelli, Błaszczyszyn, and Karray, which contains much material, including new results and proofs. Finally, Last and Penrose wrote a mathematical monograph Lectures on the Poisson process, which is freely available online here.

Signal strengths of a wireless network

In two previous posts, here and here, I discussed the importance of the quantity called the signal-to-interference ratio, which is usually abbreviated as SIR, for studying communication in wireless networks. In everyday terms, for a listener to hear a certain speaker in a room full of people speaking, the ratio of the speaker’s volume to the sum of the volumes of everyone else heard by the listener. The SIR is the communication bottleneck for any receiver and transmitter pair in a wireless network.

But the strengths (or power values) of the signals are of course also important. In this post I will detail how we can model them using a a simple network model with a single observer.

Propagation model

For a transmitter located at \(X_i\in \mathbb{R}^2\), researchers usually attempt to represent the received power of the signal \(P_i\) with a propagation model. Assuming the power is received at \(x\in \mathbb{R}^2\), this mathematical model consists of a random and a deterministic component taking the general form
$$
P_i(x)=F_i\,\ell(|X_i-x|) ,
$$
where \(\ell(r)\) is a non-negative function in \(r>0\) and \(F_i\) is a non-negative random variable.

The function \(\ell(r)\) is called the pathloss function, and common choices include \(\ell(r)=(\kappa r)^{-\beta}\) and \(\ell(r)=\kappa e^{-\beta r}\), where \(\beta>0\) and \(\kappa>0\) are model constants.

The random variables \(F_i\) represent signal phenomena such as multi-path fading and shadowing (also called shadow fading), caused by the signal interacting with the physical environment such as buildings. It is often called fading or shadowing variables.

We assume the transmitters locations \(X_1,\dots,X_n\) are on the plane \(\mathbb{R}^2\). Researchers typically assume they form a random point process or, more precisely, the realization of a random point process.

From two dimensions to one dimension

For studying wireless networks, a popular technique is to consider a wireless network from the perspective of a single observer or user. Researchers then consider the incoming or received signals from the entire network at the location of this observer or user. They do this by considering the inverses of the signal strengths, namely

$$
L_i(x): = \frac{1}{P_i}=\frac{1}{F_i \,\ell(|X_i-x|) }.
$$

Mathematically, this random function is simply a mapping from the two-dimensional plane \(\mathbb{R}^2\) to the one-dimensional non-negative real line \(\mathbb{R}_0^+=[0,\infty)\).

If the transmitters are located according to a non-random point pattern or a random point process, this random mapping generates a random point process on the non-negative real line. The resulting one-dimensional point process of the values \(L_1,L_2,\dots, \) has been called (independently) propagation (loss) process or path loss (with fading) process. More recently, my co-authors and I decided to call it a projection process, but of course the precise name doesn’t mattter

Intensity measure of signal strengths

Assuming a continuous monotonic path loss function \(\ell\) and the fading variables \(F_1, F_2\dots\) are iid, if the transmitters form a stationary random point process with intensity \(\lambda\), then the inverse signal strengths \(L_1,L_2,\dots \) form a random point process on the non-negative real line with the intensity measure \(M\).

$$
M(t) =\lambda \pi \mathbb{E}( [\ell(t F)^{-1} ]^2)\,,
$$

where \(\ell^{-1}\) is the generalized inverse of the function \(\ell\). This expression can be generalized for a non-stationary point process with general intensity measure \(\Lambda\).

The inverses \(1/L_1,1/L_2,\dots \), which are the signal strengths, forprocess with intensity measure

$$
\bar{M}(s) =\lambda \pi \mathbb{E}( [\ell( F/s)^{-1} ]^2).
$$

Poisson transmitters gives Poisson signal strengths

Assuming a continuous monotonic path loss function \(\ell\) and the fading variables \(F_1, F_2\dots\) are iid, if the transmitters form a Poisson point process with intensity \(\lambda\), then the inverse signal strengths \(L_1,L_2,\dots \) form a Poisson point process on the non-negative real line with the intensity measure \(M\).

If \(L_1,L_2,\dots \) form a homogeneous Poisson point process, then the inverses \(1/L_1,1/L_2,\dots \) will also form a Poisson point process with intensity measure \(\bar{M}(s) =\lambda \pi \mathbb{E}( [\ell( F/s)^{-1} ]^2). \)

Propagation invariance

For \(\ell(r)=(\kappa r)^{-\beta}\) , the expression for the intensity measure \(M\) reduces to
$$
M(t) = \lambda \pi t^{-2/\beta} \mathbb{E}( F^{-2/\beta})/\kappa^2.
$$

What’s striking here is that information of the fading variable \(F\) is captured simply by one moment \(\mathbb{E}( F^{-2/\beta}) \). This means that two different distributions will give the same results as long as this moment is matching. My co-authors and I have been called this observation propagation invariance.

Some history

To study just the (inverse) signal strengths as a point process on the non-negative real line was a very useful insight. It was made independently in these two papers:

  • 2008, Haenggi, A geometric interpretation of fading in wireless
    networks: Theory and applications;
  • 2010, Błaszczyszyn, Karray, and Klepper, Impact of the geometry, path-loss exponent and random shadowing on the mean interference factor in wireless cellular networks.

My co-authors and I presented a general expression for the intensity measure \(M\) in the paper:

  • 2018, Keeler, Ross and Xia, When do wireless network signals appear Poisson?.

This paper is also contains examples of various network models.

Further reading

A good starting point on this topic is the Wikipedia article Stochastic geometry models of wireless networks. The paper that my co-authors and I wrote has details on the projection process.

With Bartek Błaszczyszyn, Sayan Mukherjee, and Martin Haenggi, I co-wrote a short monograph on SINR models called Stochastic Geometry Analysis of Cellular Networks, which is written at a slightly more advanced level. The book put an emphasis on studying the point process formed from inverse signal strengths, we call the projection process.

The Standard Model of wireless networks

The previous post I discussed the signal-to-interference-plus ratio or SIR in wireless networks. If noise is included, then then signal-to-interference-plus-noise ratio or just SINR. But I will just write about SIR, as most results that hold for SIR, will also hold for SINR without any great mathematical difficulty.

The SIR is an important quantity due to reasons coming from information theory.  If you’re unfamiliar  with it, I suggest reading the previous post.

In this post, I will describe a very popular mathematical model of the SIR, which I like to call the standard model. (This is not a term used in the literature as I have borrowed it from physics.)

Definition of SIR

To define the SIR, we consider a wireless network of \(n\) transmitters with positions located at \(X_1,\dots,X_n\) in some region of space. At some location \(x\), we write \(P_i(x)\) to denote the power value of a signal received at \(x\) from transmitter  \(X_i\). Then at location \(x\), the SIR with respect to transmitter \(X_i\) is
$$
\text{SIR}(x,X_i) := \frac{P_i(x)}{\sum\limits_{j\neq i} P_j(x)} .
$$

Researchers usually attempt to represent the received power of the signal \(P_i(x)\) with a propagation model. This mathematical model  consists of a random and a deterministic component given by
$$
P_i(x)=F_i\ell(|X_i-x|) ,
$$
where \(\ell(r)\) is a non-negative function in \(r\geq 0\) and \(F_i\) is a non-negative random variable. The function \(\ell(r)\)  is often called the path loss function. The random variables represent random fading or shadowing.

Standard model

Based on the three model components of fading, path loss and transmitter locations, there are many combinations possible. That said, researchers generally (I would guess, say, 90 percent or more) use a single combination, which I call the standard model.

The three standard model assumptions are:

  1. Singular power law path loss \(\ell(r)=(\kappa r)^{-\beta}\).
  2. Exponential distribution for fading variables, which are independent and identically distributed (iid).
  3. Poisson point process for transmitter locations.

Why these three? Well, in short, because they work very well together. Incredibly, it’s sometimes possible to get relatively a simple  mathematical expression for, say, the coverage probability \(\mathbb{P}[\text{SIR}(x,X_i)>\tau ]\), where \(\tau>0\).

I’ll now detail the reasons more specifically.

Path loss

The \(\ell(r)=(\kappa r)^{-\beta}\) is very simple, despite having a singularity at \(r=0\). This allows simple algebraic manipulation of equations.

Some, such as myself, are initially skeptical of this function as it gives an infinitely strong signal at the transmitter due to the singularity in the function \(\ell(r)=(\kappa r)^{-\beta}\). More specifically, the path loss of the signal from transmitter \(X_i\) approaches infinity as \(x\) approaches \(X_i\) .

But apparently, overall, the singularity does not have a significant impact on most mathematical results, at least qualitatively. That said, one still observe consequences of this somewhat physically unrealistic model assumption. And I strongly doubt enough care is taken by researchers to observe and note this.

Fading and shadowing variables

Interestingly, the original reason why exponential variables were used is because it allowed the SIR problem to be reformulated into a problem of a Laplace transform of a random variable, which for a random variable \(Y\) is defined as

$$
\mathcal{L}_Y(t)=\mathbb{E}(e^{- Y t}) \, .
$$

where \(t\geq 0\). (This is essentially the moment-generating function with \(-t\) instead of \(t\).)

The reason for this connection is that the tail distribution of an exponential variable \(F\) with mean \(\mu\)  is simply \(\mathbb{P}(F>t)= e^{-t/\mu}\).  In short, with the exponential assumption, various conditioning arguments eventually lead to Laplace transforms of random variables.

Transmitters locations

No prizes for guessing that researcher overwhelmingly use a (homogeneous) Poisson point process for the transmitter (or receiver) locations. When developing mathematical models with point processes, if you can’t get any results with the Poisson point process, then abandon all hope.

It’s the easier to work with this point process due to its independence property, which leads to another useful property. For Poisson point process, the Palm distribution is known, which is the distribution of a point process conditioned on a point (or collection of points) existing in a specific location of the underlying space on which the point process is defined.  In general, the Palm distribution is not known for many point processes.

Random propagation effects can lead to Poisson

A lesser known reason why researchers would use the Poisson point process is that, from the perspective of a single observer in the network, it can be used to capture the randomness in the signal strengths.  Poisson approximation results in probability imply that randomly perturbing the signal strengths can make signals appear more Poisson, by which I mean  the signal strengths behave stochastically or statistically as though they were created by a Poisson network of transmitters.

The end result is that a non-Poisson network can appear more Poisson, even if the transmitters do not resemble (the realization of) a Poisson point process. The source of randomness that makes a non-Poisson network appear more Poisson is the random propagation effects of fading, shadowing, randomly varying antenna gains, and so on, or some combination of these.

Further reading

A good starting point on this topic is the Wikipedia article Stochastic geometry models of wireless networks. This paper is also good:

  • 2009, Haenggi, Andrews, Baccelli, Dousse, Franceschetti, Stochastic Geometry and Random Graphs for the Analysis and Design of Wireless Networks.

This paper by my co-authors and I has some details on standard model and why a general network model behaving Poisson in terms of the signal strengths:

  • 2018, Keeler, Ross and Xia, When do wireless network signals appear Poisson?.

Early books on the subject include the two-volume textbooks  Stochastic Geometry and Wireless Networks by François Baccelli and Bartek Błaszczyszyn, where the first volume is on theory and the second volume is on applications.  Martin Haenggi wrote a very readable introductory book called Stochastic Geometry for Wireless networks.

Finally, I co-wrote with Bartek Błaszczyszyn, Sayan Mukherjee, and Martin Haenggi a short monograph on SINR models called Stochastic Geometry Analysis of Cellular Networks, which is written at a slightly more advanced level. This book has a section on why signal strengths appear Poisson.

Signal-to-interference ratio in wireless networks

The fundamentals of information theory say that to successfully communicate across any potential communication link the signal strength of the communication must be stronger than that of the back ground noise, which leads to the fundamental quantity known as signal-to-noise ratio. Information theory holds in very general (or, in mathematical speak, abstract) settings. The communication could be, for example, a phone call on an old wired landline, two people talking in a bar, or a hand-written letter, for which the respective signals in these examples are the electrical current, speaker’s voice, and the writing. (Respective examples of noise could be, for example, thermal noise in the wires, loud music, or coffee stains on the letter.)

In wireless networks, it’s possible for a receiver to simultaneously detect signals from multiple transmitters, but the receiver typically only wants to receive one signal. The other unwanted or interfering signals form a type of noise, which is usually called interference, and the other (interfering) transmitters are called interferers. Consequently, researchers working on wireless networks study the signal-to-interference ratio, which is usually abbreviated as SIR. Another name for the SIR is carrier-to-interference ratio.

If we also include background noise, which is coming not from the interferers, then the quantity becomes the signal-to-interference-plus-noise ratio or just SINR. But I will just write about SIR, though jumping from SIR to SINR is usually not difficult mathematically.

The concept of SIR makes successful communication more difficult to model and predict, as it just doesn’t depend on the distance of the communication link. Putting the concept in everyday terms, for a listener to hear a certain speaker in a room full of people all speaking to the listener, it is not simply the distance to the speaker, but rather the ratio of the speaker’s volume to the sum of the volumes of everyone else heard by the listener. The SIR is the communication bottleneck for any receiver and transmitter pair in a wireless network.

In wireless network research, much work has been done to examine and understand communication success in terms of interference and SIR, which has led to a popular mathematical model that incorporates how signals propagate and the locations of transmitters and receivers.

Definition

To define the SIR, we consider a wireless network of transmitters with positions located at \(X_1,\dots,X_n\) in some region of space. At some location \(x\), we write \(P_i(x)\) to denote the power value of a signal received at \(x\) from transmitter \(X_i\). Then at location \(x\), the SIR with respect to transmitter \(X_i\) is
$$
\text{SIR}(x,X_i) :=\frac{P_i(x)}{\sum\limits_{j\neq i} P_j(x)} =\frac{P_i(x)}{\sum\limits_{j=1}^{n} P_j(x)-P_i(x)} .
$$

Increasing the number of transmitters \(n\) decreases the original SIR values, but then, in exchange, there is a greater number of transmitters for the receiver to connect to, some of which may have larger \(P_i(x)\) values and, subsequently, SIR values. This delicate trade-off makes it challenging and interesting to mathematically analyze and design networks that deliver high SIR values.

Researchers usually assume that the SIR is random. A quantity of interest is the tail distribution of the SIR, namely

$$
\mathbb{P}[\text{SIR}(x,X_i)>\tau ] := \frac{P_i(x)}{\sum\limits_{j\neq i} P_j(x)} \,,
$$

where \(\tau>0\) is some parameter, sometimes called the SIR threshold. For a given value of \(\tau\), the probability \(\mathbb{P}[\text{SIR}(x,X_i)>\tau]\) is sometimes called the coverage probability, which is simply the probability that a signal coming from \(X_i\) can be received successfully at location \(x\).

Mathematical models

Propagation

Researchers usually attempt to represent the received power of the signal \(P_i(x)\) with a propagation model. This mathematical model consists of a random and a deterministic component taking the general form
$$
P_i(x)=F_i\ell(|X_i-x|) ,
$$
where \(F_i\) is a non-negative random variable and \(\ell(r)\) is a non-negative function in \(r \geq 0\).

Path loss

The function \(\ell(r)\) is called the path loss function, and common choices include \(\ell(r)=(\kappa r)^{-\beta}\) and \(\ell(r)=\kappa e^{-\beta r}\), where \(\beta>0\) and \(\kappa>0\) are model constants, which need to be fitted to (or estimated with) real world data.

Researchers generally assume that the so-called path loss function \(\ell(r)\) is decreasing in \(r\), but actual path loss (that is, the change in signal strength over a path travelled) typically increases with distance \(r\). Researchers originally assumed path loss functions to be increasing, not decreasing, giving the alternative (but equivalent) propagation model
$$
P_i(x)= F_i/\ell(|X_i-x|).
$$

But nowadays researchers assume that the function \(\ell(r)\) is decreasing in \(r\). (Although, based on personal experience, there is still some disagreement on the convention.)

Fading and shadowing

With the random variable \(F_i\), researchers seek to represent signal phenomena such as multi-path fading and shadowing (also called shadow fading), caused by the signal interacting with the physical environment such as buildings. These variables are often called fading or shadowing variables, depending on what physical phenomena they are representing.

Typical distributions for fading variables include the exponential and gamma distributions, while the log-normal distribution is usually used for shadowing. The entire collection of fading or shadowing variables is nearly always assumed to be independent and identically distributed (iid), but very occasionally random fields are used to include a degree of statistical dependence between variables.

Transmitters locations

In general, we assume the transmitters locations \(X_1,\dots,X_n\) are on the plane \(\mathbb{R}^2\). To model interference, researchers initially proposed non-random models, but they were considered inaccurate and intractable. Now researchers typically use random point processes or, more precisely, the realizations of random point processes for the transmitter locations.

Not surprisingly, the first natural choice is the Poisson point process. Other point processes have been used such as Matérn and Thomas cluster point processes, and Matérn hard-core point processes, as well as determinantal point processes, which I’ll discuss in another post.

Shot noise

Interference can be considered a type of shot noise, which is sum of numbers, where each is number is given by the same function applied to a point of a point processes. More specifically, we consider a point processes \(\Phi=\{X_i\}_i\) defined on some space \(\mathbb{S}\), which is often \(\mathbb{R}^n\), and a non-negative function \(f\) with the domain \(\mathbb{S}\), so \(f:S \rightarrow [0,\infty)\). Then shot noise is defined as
$$
I= \sum_{X_i\in \Phi} f(X_i)\,.
$$

This shot noise can be generalized by considering a marked point process \(\Phi’=\{(X_i, M_i)\}_i\), where each point \(X_i\) now has a random mark \(M_i\), which can be a random variable some other random object taking values in some space \(\mathbb{M}\). Then for a function \(g:S\times \mathbb{M} \rightarrow [0,\infty)\) , the shot noise is defined as
$$
I= \sum_{X_i\in \Phi’} g(X_i,M_i)\,.
$$

Some history

Shot noise

Shot noise has been long studied in probability. In this context the first research goes back a long time, such as the pioneering results by Campbell at the beginning of the 20th century, which where Campbell’s theorem in point process theory derives its name.

There has been various probability papers studying shot noise, including:

  • 1960, Gilbert and Pollak. Amplitude distribution of shot noise;
  • 1971, Daley, The definition of a multi-dimensional generalization of shot noise;
  • 1977, Rice, On generalized shot noise;
  • 1990 Lowen and Teich, Power-law shot noise.

Wireless network models

Early random models of wireless networks go back to the 60s and 70s, but these were based simply on geometry: meaning a transmitter could communicate successfully to a receiver if they were closer than some fixed distance. Edgar Gilbert created the field of continuum percolation with this significant paper:

  • 1961, Gilbert, Random plane networks.

Interest in random geometrical models of wireless networks continued into the 70s and 80s. But there was no SIR in these models.

Motivated by understanding SIR, researchers in the late 1990s and early 2000s started tackling SIR problems by using a random model based on techniques from stochastic geometry and point processes. Early papers include:

  • 1997, Baccelli, Klein, Lebourges ,and Zuyev, Stochastic geometry and architecture of communication networks;
  • 2003, Baccelli and Błaszczyszyn , On a coverage process ranging from the Boolean model to the Poisson Voronoi tessellation, with applications to wireless communications;
  • 2006, Baccelli, Mühlethaler, and Błaszczyszyn, An Aloha protocol for multihop mobile wireless networks.

But they didn’t know that some of their results had already been discovered independently by researchers working on wireless networks in the early 1990s. These papers include:

  • 1994, Pupolin and Zorzi, Outage probability in multiple access packet radio networks in the presence of fading;
  • 1990, Sousa and Silvester, Optimum transmission ranges in a direct-sequence spread-spectrum multihop packet radio network.

The early work focused more on small-scale networks like wireless ad hoc networks. Then the focus shifted dramatically to mobile or cellular phone networks with the publication of the paper:

  • 2011, Andrews, Baccelli, Ganti, A tractable approach to coverage and rate in cellular networks.

It’s can be said with confidence that this paper inspired much of the interest in using point processes to develop models of wireless networks. The work generally considers the SINR in the downlink channel for which the incoming signals originate from the phone base stations.

Further reading

A good starting point on this topic is the Wikipedia article Stochastic geometry models of wireless networks. This paper is also good:

2009, Haenggi, Andrews, Baccelli, Dousse, Franceschetti, Stochastic Geometry and Random Graphs for the Analysis and Design of Wireless Networks.

Early books on the subject include the two-volume textbooks Stochastic Geometry and Wireless Networks by François Baccelli and Bartek Błaszczyszyn, where the first volume is on theory and the second volume is on applications. Martin Haenggi wrote a very readable introductory book called Stochastic Geometry for Wireless networks.

Finally, Bartek Błaszczyszyn, Sayan Mukherjee, Martin Haenggi, and I wrote a short book on SINR models called Stochastic Geometry Analysis of Cellular Networks, which is written at a slightly more advanced level. The book put an emphasis on studying the point process formed from inverse signal strengths, we call the projection process.

Simulating Matérn hard-core point processes

If you wanted to create a point process with repulsion, a reasonable first attempt would be to build off a Poisson point process by removing points according to some rule to ensure that no two points were within a certain distance of each other. Using this natural idea, Bertril Matérn proposed a family of repulsive point processes called Matérn hard-core point processes.

More specifically, Matérn proposed several points processes, including two types of hard-core point processes now called Type I and Type II. (Matérn proposed a third type, called Type III, but it’s considerably harder to simulate on a computer, as detailed in this article.) These types of hard-core point processes are completely different to the Matérn cluster point process.

As I discussed in a previous post, the Poisson point process may not be adequate for representing point phenomena whose points exhibit large degrees of repulsion or clustering. I already covered the Matérn and Thomas cluster point processes, which show distinct clustering in their configurations. In this post, I’ll cover Matérn hard-core point processes. The Type I point processes is the easier of the two, so I’ll start with that one.

Overview

Simulating Matérn hard-core point processes requires first simulating a homogeneous Poisson point process with an intensity \(\lambda>0\) on some simulation window, such as a rectangle, which is the simulation window I will use here. I have already written about simulating the homogeneous Poisson point processes on a rectangle and a disk, so those posts are good starting points.

Given the Poisson point process, the points then need to be thinned in such a manner to ensure that for each point, there is no other point within some fixed \(r>0\) of the point. This distance \(r>0\) is the radius of the hard core of each point.

I have already covered the point process operation of thinning. But it’s important to note here that in this construction a dependent thinning is being applied. (If I just applied an independent thinning, then the resulting point process will be another Poisson point process with no repulsion between points.)

Edge effects

The main trick behind sampling this point process is that it’s possible for points inside the simulation window to be thinned due to their closeness to points that are located outside the simulation window. In other words, points outside the simulation window can cause points inside the window to be thinned. (I discussed a very similar issue in the posts on the Matérn and Thomas cluster point processes.)

To remove these edge effects, the underlying Poisson point process must be simulated on an extended version of the simulation window. The points are then thinned according to a dependent thinning, which is covered in the next section. Then only the retained points inside the simulation window are kept and the remaining points are ignored. Consequently, the underling Poisson points are simulated on an extended window, but we only see the final points inside the simulation window.

To create the extended simulation window, we add a strip of width \(r\) all around the simulation window. Why? Well, the distance \(r\) is the maximum distance from the simulation window that another point (outside the simulation window) can exist, while still causing points inside the simulation window to be thinned. This means it is impossible for a hypothetical point beyond this distance (outside the extended window) to cause a point inside the simulation window to be thinned.

Dependent thinning rules

Type I

For each point inside the simulation window, check if there are any other points (including those in the extended window) within distance \(r\) of the point. If no, then keep the point. If yes, then remove the point and the points that are within distance \(r\) of the point. The remaining points inside the simulation window form a Matérn Type I point process.

This is a relatively simple thinning rule, which only requires calculating all the inter-point distances. But it is also a very strong thinning rule, meaning that it removes many points. Depending on the Poisson point process intensity \(\lambda\) and core radius \(r\), it is quite possible that all the points are removed, resulting in an empty configuration.

Now we examine the case when the thinning rule is not as strong.

Type II

To create Matérn Type II point process, we assign an independent uniform random variable to each point of the underlying Poisson point process defined on the extended window. In point process terminology, these random variables are called marks, resulting in a marked point process. In the the context of the Matérn Type II point process, these random random marks are usually called ages.

Then for each point in the simulation window, we consider all the points within distance \(r\) of the point. If this point is the youngest (or, equivalently, the oldest) point, then the point is kept. In other words, the point is only kept if its random mark is smaller (or larger) than the random marks of all the other points within distance \(r\) of the point. The remaining points inside the simulation window form a Matérn Type II point process.

Intensity expressions

Using point process and probability theory, one can derive mathematical expressions for the intensities (that is, the average density of points per unit area). These closed-form expressions can then be used to check that the correct number of points are being generated on average over many simulations.

Type I

The intensity of the Type I point process is given by

\[\mu_1=\lambda e^{-\lambda \pi r^2},\]

where \(\lambda \pi r^2\) is simply the area of the core.

Type II

The intensity of the Type II point process is given by

\[\mu_2=\frac{1}{\pi r^2}(1-e^{-\lambda \pi r^2}),\]

which can be written with the intensity of the the Type I point process as

\[\mu_2=\frac{1}{\pi r^2}(1-\frac{\mu_1}{\lambda}).\]

Code

I wrote the sampling code in MATLAB and Python, which are, as usual, very similar to each other. The code, which is is located here, simulates both Type I and II Matérn points processes. It also compares the empirical intensity to the the values given by the mathematical expressions in the previous section.

MATLAB

The MATLAB code is here.

Python

The Python code is here.

Results

I have plotted single realizations of the Matern Type I and II point processes, as well as the underlying Poisson point process in the same window.

MATLAB

Python

Further reading

Matérn hard-core point processes are covered in standard books on the related fields of spatial statistics, point processes and stochastic geometry, such as the following: Spatial Point Patterns: Methodology and Applications with R by Baddeley, Rubak and Turner, on page 140; Statistical Analysis and Modelling of Spatial Point Patterns Statistics by Illian, Penttinen, Stoyan, amd Stoyan, Section 6.5.2, starting on page 388; and; Stochastic Geometry and its Applications by Chiu, Stoyan, Kendall and Mecke, Section 5.4, starting on page 176. The first two books are particularly good for beginners.

The book Spatial Point Patterns: Methodology and Applications with R is written by spatial statistics experts Baddeley, Rubak and Turner. It covers the spatial statistics (and point process simulation) R-package spatstat., which has the functions rMaternI and rMaternII for simulating the two point processes respectively.

Simulating Poisson point processes faster

As an experiment, I tried to write code for simulating many realizations of a homogeneous Poisson point process in very fast fashion. My idea was to simulate all the realizations in two short steps.

In reality, the findings of this experiment and the contents of this post have little practical value, as computers are so fast at generating Poisson point processes. Still, it was an interesting task, which taught me a couple of things. And I did produce faster code.

MATLAB

I first tried this experiment in MATLAB.

Vectorization

In languages like MATLAB, the trick for speed is to use vectorization, which means applying a single operation to an entire vector or matrix (or array) without using an explicit for-loop. Over the years, the people behind MATLAB has advised to use vectorization instead of for-loops, as for-loops considerably slowed down MATLAB code. (But, as as time goes by, it seems using for-loops in MATLAB doesn’t slow the code down as much as it used to.)

Simulating Poisson point processes is particularly amenable to vectorization, due to the independent nature of the point process. One can simulate the number of points in each realization for all realizations in one step. Then all the points across all realizations can also be positioned in one step. In the two-dimensional case, this results in two one-dimensional arrays (or vectors, in MATLAB parlance) for the \(x\) and \(y\) coordinates.  (Of course, in my code, I could have used just one two-dimensional array/vector  for the coordinates of the points, but I didn’t.)

After generating the points, the coordinates of the points need to be grouped into the different realizations and stored in appropriate data structures.

Data structures

The problem with storing point processes is that usually each realization has a different number of points, so more sophisticated data structures than regular arrays are needed. For MATLAB, each point process realization can be stored in a data object called a cell array.  For a structure array, it’s possible for each element (that is, structure) to be a different size, making them ideal for storing objects like point processes with randomly varying sizes.

In the case of two-dimensional point processes, two cell arrays  can be used to store the \(x\) and \(y\) coordinates of all the point process realizations. After randomly positioning all the points, they can be grouped into a cell array, where each cell array element represents a realization of the Poisson point process, by using the inbuilt function MATLAB mat2cell, which converts a matrix (or array) into a cell array.

Alternatively, we could use another MATLAB data object called a structure array. In MATLAB structures have fields, which can be, for example for a point process can be the locations of the points. Given cell arrays of equal size, we can convert them into a single structure array by using the inbuilt MATLAB function struct.

Python

After successfully simulating Poisson point processes in MATLAB, I tried it in Python with the NumPy library.

Vectorization

I basically replicated what I did in MATLB using Python by positioning all the points in a single step. This gives two one-dimensional NumPy arrays for the \(x\) and \(y\) coordinates of all the point process realizations. (Again, I could have instead stored the coordinates as a single two-dimensional array, but I didn’t.)

Perhaps surprisingly, the vectorization step doesn’t speed things up much in Python with the NumPy library. This may be due to the fact that the underlying code is actually written in the C language.  That motivated me to see what methods have been implemented for simulating Poisson variables, which is the topic of the next couple posts.

Data structures

In Python, the data structure  known as a list is the natural choice. Similarly to cell arrays in MATLAB, two lists can be used for the \(x\) and \(y\) coordinates of all the points. Instead of MATLAB’s function mat2cell, I used the NumPy function numpy.split to create two lists from the two NumPy arrays containing the point coordinates.

Python does not seem to have an immediate equivalent to structure arrays in MATLAB. But in Python one can define a new data structure or class with fields, like a structure. Then one can create a list of those data structures with fields, which are called attribute references in Python.

Code

The code in MATLAB and Python can be found here. For a comparison, I also generated the same number of point process realizations (without using vectorization) by using a trusty for-loop. The code compares the times of taken for implemented the two different approaches, labelled internally as Method A and Method B. There is a some time difference in the MATLAB code, but not much of a difference in the Python case.

I have commented out the sections that create data structures (with fields or attribute references) for storing all the point process realizations, but those sections should also work when uncommented.

Placing a random point uniformly in a Voronoi cell

In the previous post, I discussed how Voronoi or Dirichlet tesselations are useful and how they can be calculated or estimated with many scientific programming languages by using standard libraries usually based on Qhull. The cells of such a tessellation divide the underlying space. Now I want to randomly place a single point in a uniform manner in each bounded Voronoi cell.

But why?

Well, this task arises occasionally, particularly when developing mathematical models of wireless networks, such as mobile or cellular phone networks. A former colleague of mine had to do this, which inspired me to write some MATLAB code a couple of years ago. And I’ve seen the question posed a couple of times on the web . So I thought: I can do that.

Overview

For this problem, I see two obvious methods.

Simple but crude

The simplest method is to cover each Voronoi cell with a rectangle or disk. Then randomly place a point uniformly on the rectangle or disk. If it doesn’t randomly land inside the rectangle or disk, then do it again until it does. Crude, slightly inefficient, but simple.

Elegant but slightly tricky

A more elegant way, which I will implement, is to partition (or divide) each Voronoi cell into triangles. Then randomly choose a triangle based on its area and, finally, uniformly position a point on that triangle.

Method

Partition cells into triangles

It is straightforward to divide a Voronoi cell into triangles. Each side of the cell corresponds to one side of a triangle (that is, two points). The third point of the triangle is the original point corresponding to the Voronoi cell.

Choose a triangle

Randomly choosing a triangle is also easy. For a given cell, number the triangles. Which random triangle is chosen is simply a discrete random variable whose probability mass function is formed from triangle areas normalized (or divided) by the total area of the Voronoi cell. In other words, the probability of randomly choosing triangle \(i\) with area \(A_i\) from \(m\) triangles is simply

\(P_i=\frac{A_i}{\sum_{j=1}^m A_j}.\)

To calculate the area of the triangles, I use the shoelace formula , which for a triangle with corners labelled \(\textbf{A}\), \(\textbf{B}\) and \(\textbf{C}\) gives

\(A= \frac{1}{2} |(x_{\textbf{B}}-x_{\textbf{A}})(y_{\textbf{C}}-y_{\textbf{A}})-(x_{\textbf{C}}-x_{\textbf{A}})(y_{\textbf{B}}-y_{\textbf{A}})|.\)

But you can also use Herron’s formula.

Then the random variable is sampled using the probabilities based on the triangle areas.

Place point on chosen triangle

Given a triangle, the final step is also easy, if you know how, which is often the case in mathematics. I have already covered this step in a previous post, but I’ll give some details here.

To position a single point uniformly in the triangle, generate two random uniform variables on the unit interval \((0,1)\), say \(U\) and \(V\). The random \(X\) and \(Y\) coordinates of the single point are given by the expressions:

\(X=\sqrt{U} x_{\textbf{A}}+\sqrt{U}(1-V x_{\textbf{B}})+\sqrt{U}V x_{\textbf{C}}\)

\(Y=\sqrt{U} y_{\textbf{A}}+\sqrt{U}(1-V y_{\textbf{B}})+\sqrt{U}V y_{\textbf{C}}\)

Results

The blue points are the points of the underlying point pattern that was used to generate the Voronoi tesselation. (These points have also been arbitrarily numbered.) The red points are the random points that have been uniformly placed in all the bounded Voronoi cells.

MATLAB

Python

Empirical validation

We can empirically validate that the points are being placed uniformly on the bounded Voronoi cells. For a given (that is, non-random) Voronoi cell, we can repeatedly place (or sample) a random point uniformly in the cell. Increasing the number of randomly placed points, the respective averages of the \(x\) and \(y\) coordinates of the points will converge to the centroids (or geometric centres) of the Voronoi cell, which can be calculated with simple formulas.

Code

The code for all my posts is located online here. For this post, the code in MATLAB and Python is here.

I have also written in MATLAB and Python the code as functions (in files funVoronoiUniform.m and funVoronoiUniform.py, respectively), so people can use it more easily. The function files are located here, where I have also included an implementation of the aforementioned empirical test involving centroids. You should be able to use those functions and for any two-dimensional point patterns.

Further reading

Much has been written on Voronoi or Dirichlet tessellations, particularly when the seeds of the cells form a Poisson point process. For references, I recommend my previous post, where I say that the references in the articles on Wikipedia and MathWorld are good starting points.

In this StackExchange post, people discuss how to place a single point uniformly on a general triangle. For the formulas, the thread cites the paper Shape distributions by Osada, Funkhouser, Chazelle and Dobkin, where no proof is given.

Voronoi tessellations

Cholera outbreaks due to public water pumps. Suburbs serviced by hospitals. Formation of crystals. Coverage regions of phone towers. We can model or approximate all these phenomena and many, many more with a geometric structure called, among other names, a Voronoi tessellation.

The main other name for this object is the Dirichlet tessellation. Historically, Dirichlet beats Voronoi, but it seems wherever I look, the name Voronoi usually wins out, suggesting an example of Stigler’s law of eponymy. A notable exception is the R library spatstat that does actually call it a Dirichlet tessellation. Wikipedia calls it Voronoi diagram. I’ve read that Descartes studied the object even earlier than Dirichlet, but Voronoi studied it in much more depth. I will call it a Voronoi tessellation.

To form a Voronoi tessellation, consider a collection of points positioned or scattered on some space, like the plane, where it’s easier to picture things, especially when using a Euclidean metric. Now for each point in the collection, consider the region surrounding the point that is closer to that point than any other point in the collection. Each region forms a cell corresponding to the point. The union of all the sets covers the underlying space.

The evolution of Voronoi cells, which start off as disks until they collide with each other. Source: Wikipedia.

 

Everyday Voronoi tesselations

Voronoi tesselations are just not interesting mathematical objects, as they arise in everyday situations. This piece from Scientific American website explains:

Everyone uses Voronoi tessellations, even without realizing it. Individuals seeking the nearest café, urban planners determining service area for hospitals, and regional planners outlining school districts all consider Voronoi tessellations. Each café, school, or hospital is a site from which a Voronoi tessellation is generated. The cells represent ideal service areas for individual businesses, schools, or hospitals to minimize clientele transit time. Coffee drinkers, patients, or students inside a service area (that is, a cell) should live closer to their own café, hospital, or school (that is, their own cell’s site) than any other. Voronoi tessellations are ubiquitous, yet often invisible.

Delaunay triangulation

A closely related object to the Voronoi tessellation is the Delaunay triangulation. For a given collection of points on some underlying mathematical space, a Delaunay triangulation is formed by connecting the points and creating triangles with the condition that for each point, no other point exists in the circumcircle of the corresponding triangle. (A circumcircle is a circle that passes through all three vertices of a triangle.)

An example of Delaunay triangulation with the original points in black and centrers (in red) of the corresponding circumcircles (in grey) of the Delaunay triangles. Source: Wikipedia.

The vertices of the the Delaunay triangular and Voronoi tessellation both form graphs, which turn out to be the dual graphs of each other.

A Delaunay triangulation (in black) and the corresponding Voronoi tessellation (in red) whose vertices are the centres of the circumcircles of the Delaunay triangles. Source: Wikipedia.

Software: Qhull

Due to their applications, it’s not surprising that there exist algorithms that quickly create or estimate Voronoi tessellations. I don’t want to implement one of these algorithms from scratch, as they have already been implemented in various scientific programming languages. Many of the languages, such as MATLAB, R, and Python (SciPy) use the code from Qhull. (Note the website calls the tessellation a Voronoi diagram.)

(The Julia programming language, which I examined in in a previous post, has a Voronoi package that does not use Qhull.)

Qhull finds the Voronoi tessellation by first finding the Delaunay triangulation. If the underlying space is bounded, then all the Voronoi cells around bounded. But on unbounded space, it is possible to have unbounded cells, meaning their area (or volume) is infinite. In such cases, the algorithms sometimes place virtual points at infinity, but I don’t want to focus on such details. I will just assume Qhull does a good job.

Code

As always, the code from all my posts is online. For this post, the MATLAB and Python code is here and here, respectively.

MATLAB

It is fairly straightforward to create Voronoi tessellations in MATLAB. You can just use the function voronoi, which is only for two-dimensional tessellations. (Note: the MATLAB website says the behaviour of the function voronoi has changed, so that may cause problems when using different versions of MATLAB.) The function requires two inputs as vectors, say, x and y, corresponding to the Cartesian (or \(x\) and \(y\)) coordinates of the points. If you just run the voronoi command, it will create and plot a Voronoi tessellation (or Voronoi diagram, as MATLAB calls it). But the MATLAB website also describes how to plot the tessellation manually.

For d-dimensional tessellations, there is the function voronoin, which requires a single input. The single output consists of combining \(d\) column vectors for the Cartesian coordinates. For example, given the column vectors x, y and z, then the input is [x, y, z].

If you give the functions voronoi or voronoin output arguments, then the tessellation is not plotted and instead two data structures, say, v and c are created for describing the vertices of the tessellation. I generally use voronoi for plotting, but I use voronoin (and not voronoi) for generating vertex data, so I will focus on the outputs of voronoin.

For voronoin, the first (output) data structure v is simply an two-dimensional array array that contain the Cartesian coordinates of every vertex in the Voronoi tessellation. The second (output) data structure c is a cell array describing the vertices of each Voronoi cell (it has to be a cell array, as opposed to a regular array, as the cells have varying number of vertices). Each entry of the cell array contains a one-dimensional array with array indices corresponding to the \(x\) and \(y\) coordinates.

The code checks which Voronoi cells are unbounded by seeing if they have vertices at infinity, which corresponds to a \(1\) in the index arrays (stored in the structure array c).

Python

To create the Voronoi tessellation, use the SciPy (Spatial) function Voronoi. This function does \(d\)-dimensional tessellations. For the two-dimensional setting, you need to input the \(x\) and \(y\) coordinates as a single array of dimensions \(2 \times n\), where \(n\) is the number of points in the collection. In my code, I start off with two one-dimensional arrays for the Cartesian coordinates, but then I combined them into a single array by using the function numpy.stack with the function argument axis =1.

I would argue that the Voronoi function in SciPy is not as intuitive to use as the MATLAB version. For example, one thing I found a bit tricky, at first, is that the cells and the points have a different sets of numbering (that is, they are indexed differently). (And I am not the only one that was caught by this.) You must use the attribute called point_region to access a cell number (or index) from a point number (or index). In my code attribute is accessed and then called it indexP2C, which is an integer array with cell indices. Apart from that, the function Voronoi worked well.

To plot the Voronoi tessellation, use the SciPy function voronoi_plot_2d, which allows for various plotting options, but it does require Matplotlib. The input is the data object created by the function Voronoi.

Results

I’ve plotted the results for a single realization of a Poisson point process. I’ve also plotted the indices of the points. Recall that the indexing in Python and MATLAB start respectively at zero and one.

MATLAB

Python

Voronoi animations

I took the animation of evolving Voronoi cells, which appears in the introduction, from Wikipedia. The creator generated it in MATLAB and also posted the code online. The code is long, and I wouldn’t even dare to try to reproduce it, but I am glad someone else wrote it.

Such animations exist also for other metrics. For example, the Manhattan metric (or taxi cab or city block metric) gives the following animations, where the growing disks have been replaced with squares.

This Wikipedia user page has animations under other metrics on Euclidean space.

This post also animations of Voronoi tessellations when the points move.

Further reading

There is a lot of literature on Voronoi or Dirichlet tessellations, particularly when the seeds of the cells form a Poisson point process. The references in the articles on Wikipedia and MathWorld are good starting points.

Here is a good post on Voronoi tesselations. Here is a non-mathematical article published in the Irish Times.

For the more mathematically inclined, there is also the monograph Lectures on random Voronoi tessellations by Møller.

Simulating a Cox point process based on a Poisson line process

In the previous post, I described how to simulate a Poisson line process, which in turn was done by using insight from an earlier post on the Bertrand paradox.

Now, given a Poisson line process, for each line, if we generate an independent one-dimensional Poisson point point process on each line, then we obtain an example of a Cox point process. Cox point processes are also known as doubly stochastic Poisson point processes. On the topic of names, Guttorp and Thorarinsdottir argue that it should be called the Quenouille point process, as Maurice Quenouille introduced an example of it before Sir David Cox, but I opt for the more common name.

Cox point proceesses

A Cox point process is a generalization of a Poisson point process. It is created by first considering a non-negative random measure, sometimes called a driving measure. Then a Poisson point process, which is independent of the random driving measure, is generated by using the random measure as its intensity or mean measure.

The driving measure of a Cox point process can be, for example, a non-negative random variable or field multiplied by a Lebesgue measure. In our case, the random measure is the underlying Poisson line process coupled with the Lebesgue measure on the line (that is, length).

Cox processes form a very large and general family of point processes, which exhibit clustering. In previous posts, I have covered two special cases of Cox point processes: the Matérn and Thomas cluster point processes. These are, more specifically, examples of a Neyman-Scott point process, which is a special case of a shot noise Cox point process. These two point processes are fairly easy to simulate, but that’s not the case for Cox point processes in general. Some are considerably easier than others.

Motivation

I will focus on simulating the Cox point process formed from a Poisson line process with homogeneous Poisson point processes. I do this for two reasons. First, it’s easy to simulate, given we can simulate a Poisson line process. Second, it has been used and studied recently in the mathematics and engineering literature for investigating wireless communication networks in cities, where the streets correspond to Poisson lines; for example, see these two preprints:

  1. Continuum percolation for Cox point processes
  2. Poisson Cox Point Processes for Vehicular Networks

Incidentally, I don’t know what to call this particular Cox point process. A Cox line-point process? A Cox-Poisson line-point process? But it doesn’t matter for simulation purposes.

Method

We will simulate the Cox (-Poisson line-) point process on a disk. Why a disk? I suggest reading the previous posts on the Poisson line process the Bertrand paradox for why the disk is a natural simulation window for line processes.

Provided we can simulate a Poisson line process, the simulation method is quite straightforward, as I have essentially already described it.

Line process

First simulate a Poisson line process on a disk. We recall that for each line of the line process, we need to generate two independent random variables \(\Theta\) and \(P\) describing the position of the line. The first random variable \(\Theta\) gives the line orientation, and it is a uniform random variable on the interval \((0,2\pi)\).

The second random variables \(P\) gives the distance from the origin to the disk edge, and it is a uniform random variable on the interval \((0,r)\), where \(r\) is the radius of the disk. The distance from the point \((\Theta, P)\) to the disk edge (that is, the circle) along the chord is:

$$Q=\sqrt{r^2-P^2}.$$

The endpoints of the chord (that is, the points on the disk edge) are then:

Point 1: \(X_1=P \cos \Theta+ Q\sin \Theta\), \(Y_1= P \sin \Theta- Q\cos \Theta\),

Point 2: \(X_2=P \cos \Theta- Q\sin \Theta\), \(Y_2= P \sin \Theta+Q \cos \Theta\).

The length of the line segment is \(2 Q\). We can say this random line is described by the point \((\Theta,P)\).

One-dimensional Poisson point process

For each line (segment) in the line process, simulate a one-dimensional Poisson point process on it. Although I have never discussed how to simulate a one-dimensional (homogeneous) Poisson point process, it’s essentially one dimension less than simulating a homogeneous Poisson point process on a rectangle.

More specifically, given a line segment \((\Theta,P)=(\theta,p)\), you simulate a homogeneous Poisson point process with intensity \(\mu\) on a line segment with length \(2 q\), where \(q=\sqrt{r^2-p^2}\). (I am now using lowercase letters to stress that the line is no longer random.) To simulate the homogeneous Poisson point process, you generate a Poisson random variable with parameter \(2 \mu q\).

Now you need to place the points uniformly on the line segment. To do this, consider a single point on a single line. For this point, generate a single uniform variable \(U\) on the interval \((-1,1)\). The tricky part is now getting the Cartesian coordinates right. But the above expressions for the endpoints suggest that the single random point has the Cartesian coordinates:

\(x=p \cos \theta+ U q\sin \theta\), \(y=p \sin \theta- U q\cos \theta\).

The two extreme cases of the uniform random variable \(U\) (that is, \(U=-1\) and \(U=1\)) correspond to the two endpoints of the line segment. We recall that \(Q\) is the distance from the midpoint of the line segment to the disk edge along the line segment, so it makes sense that we want to vary this distance uniformly in order to uniformly place a point on the line segment. This uniform placement step is done for all the points of the homogeneous Point process on that line segment.

You repeat this procedure for every line segment. And that’s it: a Cox point process built upon a Poisson line process.

Results

MATLAB

R

Python

Code

As always, the code from all my posts is online. For this post, I have written the code in MATLAB, R and Python.

Further reading

For the first step, the reading material is basically the same as that for the Poisson line process, which overlaps with that of the Bertrand paradox. For the one-dimensional Poisson point process, we can use the reading material on the homogeneous Poisson point process on a rectangle.

For general Cox point processes, I recommend starting with the following: Chapter 6 in the monograph Poisson processes by Kingman; Chapter 5 in Statistical Inference and Simulation for Spatial Point Processes by Møller and Waagepetersen; and Section 5.2 in Stochastic Geometry and its Applications by Chiu, Stoyan, Kendall and Mecke. For a much more mathematical treatment, see Chapter 13 in Lectures on the Poisson Process by Last and Penrose.

For this particularly Cox point process, see the two aforementioned preprints, located here and here.

Simulating a Poisson line process

Instead of points, we can consider other objects randomly scattered on some underlying mathematical space. If we take a Poisson point process, then we can use (infinitely long) straight lines instead of points, giving a Poisson line process. Researchers have studied and used this random object to model physical phenomena. In this post I’ll cover how to simulate a homogeneous Poisson line process in MATLAB, R and Python. The code which can be downloaded from here

Overview

For simulating a Poisson line process, the key question is how to randomly position the lines.  This is related to a classic problem in probability theory called the Bertrand paradox.  I discussed this illustration in probability in a previous post, where I also included code for simulating it. I highly recommend reading that post first.

The Bertrand paradox involves three methods for positioning random lines. We use Method 2 to achieve a uniform positioning of lines, meaning the number of lines and orientation of lines is statistically uniform. Then it also makes sense to use this method for simulating a homogeneous (or uniform) Poisson line process.  

We can interpret a line process as a point process. For a line process on the plane \(\textbf{R}^2\), it can be described by a point process on \((0,\infty)\times (0,2\pi)\), which is an an infinitely long cylinder. In other words, the Poisson line process can be described as a Poisson point process.

For simulating a Poisson line process, it turns out the disk is the most natural setting. (Again, this goes back to the Bertrand paradox.) More specifically, how the (infinitely long) lines intersect a disk of a fixed radius \(r>0\). The problem of simulating a Poisson line process reduces to randomly placing chords in a disk. For other simulation windows in the plane, we can always bound any non-circular region with a sufficiently large disk.

Steps

Number of lines

Of course, with all things Poisson, the number of lines will be  a Poisson random variable, but what will its parameter be? This homogeneous (or uniform) Poisson line process forms a one-dimensional homogeneous (or uniform) Poisson point process around the edge of the disk with a circumference \(2 \pi r \). Then the number of lines is simply a Poisson variable with parameter \(\lambda 2 \pi r \).

Locations of points

To position a single line uniformly in a disk, we need to generate two uniform random variables. One random variable gives the angle describing orientation of the line, so it’s a uniform random variable \(\Theta\) on the interval \((0,2\pi)\). 

The other random variable gives the distance from the origin to the disk edge, meaning it’s a uniform random variable \(P\) on the interval \((0,r)\), where \(r\) is the radius of the disk.  The random radius and its perpendicular chord create a right-angle triangle.  The distance from the point \((\Theta, P)\) to the disk edge (that is, the circle) along the chord is:

$$Q=\sqrt{r^2-P^2}.$$

The endpoints of the chord (that is, the points on the disk edge) are then:

Point 1: \(X_1=P \cos \Theta+ Q\sin \Theta\), \(Y_1= P \sin \Theta- Q\cos \Theta\),

Point 2: \(X_2=P \cos \Theta- Q\sin \Theta\), \(Y_2= P \sin \Theta+Q \cos \Theta\).

Code

I have implemented the simulation procedure in MATLAB, R and Python, which, as usual, are all very similar. I haven’t put my code here, because the software behind my website keeps mangling it.  As always, I have uploaded my code to a repository; for this post, it’s in this directory.

I have written the code in R, but I wouldn’t use it in general. That’s because if you’re using R, then, as I have said before, I strongly recommend using the powerful spatial statistics library spatstat. For a simulating Poisson line process, there’s the function rpoisline.  

The chief author of spatstat, Adrian Baddeley, has written various lectures and books on the related topics of point processes, spatial statistics, and geometric probability. In this post, he answered why the angular coordinates have to be chosen uniformly. 

Results

MATLAB

R

Python

Further reading

To read about the Poisson line process, it’s best to start with the Bertrand problem, which is covered in many works on geometric probability and related fields. A good and recent introduction is given by Calka in (Section 1.3) of the lectures titled Stochastic Geometry: Modern Research Frontiers, which were edited by Coupier and published by Springer.  Also see, for example, problem 1.2 in Geometrical Probability by Kendall and Moran or page 44 in Geometric Probability by Solomon.  

For the Poisson line process, I highly recommend Section 7.2 in the monograph Poisson processes by Kingman. Also see Example 8.2 in the standard textbook Stochastic Geometry and its Applications by Chiu, Stoyan, Kendall and Mecke. The Poisson line process book also appears in Exercise 5.2 in the book Stochastic Simulation – Algorithms and Analysis by  Asmussen and Glynn. 

For online resources, this set of lectures by Christian Lantuéjoul
covers the Poisson line process. Wilfrid Kendall covers the Poisson line process in this talk in relation to so-called Poisson cities.