shot noise Archives – H. Paul Keeler

Cox point process

In previous posts I have often stressed the importance of the Poisson point process as a mathematical model. But it can be unsuitable for certain mathematical models. We can generalize it by first considering a non-negative random measure, called a driving or directing 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. This doubly stochastic construction gives what is called a Cox point process.

In practice we don’t typically observe the driving measure. This means that it’s impossible to distinguish a Cox point process from a Poisson point process if there’s only one realization available. By conditioning on the random driving measure, we can use the properties of the Poisson point process to derive those of the resulting Cox point process.

By the way, Cox point processes are also known as doubly stochastic Poisson point processes. Guttorp and Thorarinsdottir argue that we should call them the Quenouille point processes, as Maurice Quenouille introduced an example of it before Sir David Cox. But I opt for the more common name.

In this post I’ll cover a couple examples of Cox point processes. But first I will need to give a more precise mathematical definition.

Definition

We consider a point process defined on some underlying mathematical space $\mathbb{S}$, which is sometimes called the carrier space or state space. The underlying space is often the real line $\mathbb{R}$, the plane $\mathbb{R}^2$, or some other familiar mathematical space like a square lattice.

For the first definition, we use the concept of a random measure.

Let $M$ be a non-negative random measure on $\mathbb{S} $. Then a point process $\Phi$ defined on some underlying space $\mathbb{S}$ is a Cox point process driven by the intensity measure $M$ if the conditional distribution of $\Phi$ is a Poisson point process with intensity function $M$.

We can give a slightly less general definition of a Cox point process by using a random intensity function.

Let $Z=\{Z(x):x\in\mathbb{S} \}$ be a non-negative random field such that with probability one, $x\rightarrow Z(x)$ is a locally integrable function. Then a point process $\Phi$ defined on some underlying space $\mathbb{S}$ is a Cox point process driven by $Z$ if the conditional distribution of $\Phi$ is a Poisson point process with intensity function $Z$.

The random driving measure $M$ is then simply the integral
$$
M(B)=\int_B Z(x)\, dx , \quad B\subseteq S.
$$

Over-dispersion

The random driving measures take different forms, giving different Cox point processes. But there is a general observation that can be made for all Cox point processes. For any region $B \subseteq S$, it can be shown that the number of points $\Phi (B)$ adheres to the inequality
$$
\mathbb{Var} [\Phi (B)] \geq \mathbb{E} [\Phi (B)],
$$

where $\mathbb{Var} [\Phi (B)] $ is the variance of the random variable $\Phi (B)$. As a comparison, for a Poisson point process $\Phi’$, the variance of $\Phi’ (B)$ is simply $\mathbb{Var} [\Phi’ (B)] =\mathbb{E} [\Phi’ (B)]$. Due to its greater variance, the Cox point process is said to be over-dispersed compared to the Poisson point process.

Special cases

There is an virtually unlimited number of ways to define a random driving measure, where each one yields a different a Cox point process. But in general we are restricted by examining only tractable and interesting Cox point processes. I will give some common examples, but I stress that the Cox point process family is very large.

Mixed Poisson point process

For the random driving measure $M$, an obvious example is the product form $M= Y \mu $, where $Y$ is some independent non-negative random variable and $\mu$ is the Lebesgue measure on $\mathbb{S}$. This driving measure gives the mixed Poisson point process. The random variable $Y$ is the only source of randomness.

Log-Gaussian Cox point process

Instead of a random variable, we can use a non-negative random field to define a random driving measure. We then have the product $M= Y \mu $, where $Y$ is now some independent non-negative random field. (A random field is a collection of random variables indexed by some set, which in this case is the underlying space $\mathbb{S}$.)

Arguably the most tractable and used random field is the Gaussian random field. This random field, like Gaussian or normal random variables, takes both negative and positive values. But if we define the random field such that its logarithm is a Gaussian field $Z$, then we obtain the non-negative random driving measure $M=\mu e^Z $, giving the log-Gaussian Cox point process.

This point process has found applications in spatial statistics.

Cox-Poisson line-point process

To construct this Cox point process, we first consider a Poisson line process, which I discussed previously. Given a Poisson line process, we then place an independent one-dimensional Poisson point process on each line. We then obtain an example of a Cox point process, which we could call a Cox line-point process or a Cox-Poisson line-point process. (But I am not sure of the best name.)

Researchers have recently used this point process to study wireless communication networks in cities, where the streets correspond to Poisson lines. For example, see these two preprints:

Shot-noise Cox point process

We construct the next Cox point process by first considering a Poisson point process on the space $\mathbb{S}$ to create a shot noise term. (Shot noise is just the sum of some function over all the points of a point process.) We then use it as the driving measure of the Cox point process.

More specifically, we first introduce a kernel function $k(\cdot,\cdot)$ on $\mathbb{S}$, where $k(x,\cdot)$ is a probability density function for all points $x\in \mathbb{S}$. We then consider a Poisson point process $\Phi’$ on $\mathbb{S}\times (0,\infty)$. We assume the Poisson point process $\Phi’$ has a locally integrable intensity function $\mu $.

(We can interpret the point process $\Phi’$ as a spatially-dependent marked Poisson point process, where the unmarked Poisson point process is defined on $\mathbb{S}$. We then assume each point $X$ of this unmarked point process has a mark $T \in (0,\infty)$ with probability density $\mu(X,t)$.)

The resulting shot noise

$$
Z(x)= \sum_{(Y,T)\in \Phi’} T \, k(Y,x)\,,
$$

gives the random field. We then use it as the random intensity function to drive the shot-noise Cox point process.

In previous posts, I have detailed how to simulate non-Poisson point processes such as the Matérn and Thomas cluster point processes. These are examples of a Neyman-Scott point process, which is a special case of a shot noise Cox point process. All these point processes find applications in spatial statistics.

Simulation

Unfortunately, there is no universal way to simulate all Cox point processes. (And even if there were one, it would not be the most optimal way for every Cox point process.) The simulation method depends on how the Cox point process is constructed, which usually means how its directing or driving measure is defined.

In previous posts I have presented ways (with code) to simulate these Cox point processes:

Matérn (cluster) point processes (code);
Thomas (cluster) point processes (code);
Cox-Poisson line-point process (code).

In addition to the Matérn and Thomas point processes, there are ways to simulate more general shot noise Cox point processes. I will cover that in another post.

The Laplace functional

When working with random variables, a couple useful tools are the characteristic function and the moment-generating function, which for a random variable $Y$ are defined respectively as
$$
\phi_Y(t)= \mathbb{E}\left [ e^{itY} \right ]\,
$$
and
$$
M_Y(t)= \mathbb{E}\left [ e^{tY} \right ]\,,
$$
where the imaginary number $i=\sqrt{-1}$ and the real variable $t\in \mathbb{R}$. For continuous random variables, these two respective functions are essentially the Fourier and Laplace transforms of the probability densities. (The moment-generating function $M(t)$ may not exist due to the integral not converging to a finite value, whereas the characteristic function $\phi_Y(t)$ always exists.)

If $Y$ is a discrete random variable, a useful object is the probability-generating function, which is defined as
$$
G_Y(z)= \mathbb{E}\left [z^Y \right ]\,.
$$
This function is the Z -transform of the probability mass function of the random variable $Y$.

By using these tools, results such as sums of random variables and convergence theorems can be proven. There exist equivalent tools which prove useful for studying point processes (and, more generally random measures).

Laplace functional

For a point process $\Phi $ defined on some underlying space $\mathbb{S}$, such as $\mathbb{R}^d$, the Laplace functional is defined as
$$
L_{\Phi}(f)=\mathbb{E}[e^{-\int_{ \mathbb{S}} f(x){\Phi}(dx)}]\,,
$$
where $f$ is any (Borel) measurable non-negative function on the space $\mathbb{S}$.

A simple point process is one for which two or more points coincide in location with probability zero. For a simple point process, we can write the random integral (or sum) using set theory notation, giving
$$
\int_{\mathbb{S}} f(x){\Phi}(dx)=\sum\limits_{x\in \Phi} f(x) \,.
$$

Name

Why’s it called a Laplace functional? From its definition, it’s clear that the first half of the name stems from the Laplace transform. Mapping from the space $\mathbb{S}$, it’s called a functional because it is a function of a non-negative function $f$.

Characterization

The Laplace functional characterizes the point process, meaning each point process (or, more generally, random measure) has its own unique Laplace functional. For a given point process, the challenge is to derive the mathematical expression for the Laplace functional by using its definition.

Poisson example

For deriving the Laplace functional, perhaps not surprisingly, one of the easiest one of the easiest point processes is the Poisson point process due to its independence property. For a Poisson process $\Phi$ with intensity measure $\Lambda$ defined on the state space $\mathbb{S}$, the Laplace functional is given by
$$
L_{\Phi}(f)=e^{-\int_{ \mathbb{S}} [1-e^{-f(x)}]\,\Lambda(dx) } \,.
$$

If the Poisson point process is homogeneous, then

$$
L_{\Phi}(f)=e^{-\lambda\int_{ \mathbb{S}} [1-e^{-f(x)}]\,dx } \,,
$$

where $\lambda$ is the intensity function (that is, the average density of points).

Applications

Proof techniques

Given a Laplace functional characterizes a point process, it can be used prove results on the distributions of point processes, where the proofs often simpler. For example, it can used to see what happens when you perform a point process operation on a point process, such as proving that the independent thinning a Poisson point process gives another Poisson point process. Laplace functionals are used to prove results on the superposition and (random or deterministic) mapping of point processes.

Interference in wireless network models

In the previous post, I covered the concept of the signal-to-interference ratio or SIR in wireless networks. (If noise is included, then then signal-to-interference-plus-noise ratio or just SINR.) Under such wireless network models, the interference term is a type of shot noise of the point process used for the transmitter locations.

Researchers commonly assume Rayleigh fading of the signal energy, which corresponds to the power values randomly varying according to an exponential distribution (due to a square root being taken). The tail distribution of an exponential variable $F$ with mean $\mu$ is simply $\mathbb{P}(F>t)= e^{-t/\mu}$. This means that the exponential assumption and some conditioning arguments lead to Laplace transforms of random variables, including the interference, which can be recast as the Laplace functional of the point process used for the transmitter locations.

Related functionals

For random variables, the characteristic, moment-generating, and probability-generating functions are similarly defined and closely related. We now define two other functionals used for studying point processes.

Characteristic functional

For a point process $\Phi $ defined on $\mathbb{S}$, the characteristic functional is defined as
$$
L_{\Phi}(f)=\mathbb{E}[e^{i\int_{ \mathbb{S}} g(x){\Phi}(dx)}]\,,
$$
where $i=\sqrt{-1}$ and $g$ is any (Borel) measurable function on the space $\mathbb{S}$.

Probability-generating functional

For a point process $\Phi $ defined on $\mathbb{S}$, the probability-generating functional is defined as
$$
G_{\Phi}(v)=\mathbb{E}[ \prod_{x\in \Phi } v(x)]\,,
$$
where $v$ is any bounded non-negative (Borel) measurable function on the space $\mathbb{S}$ such that $0\leq v(x)\leq 1$ for any point $x\in \mathbb{S}$. (Some authors use an alternative definition with a function $u(x)=1-v(x)$.)

Campbell’s theorem (formula)

In a previous post, I wrote about the concept of shot noise of a point process. In the simplest terms, shot noise is just the sum of some function over all the points of a point process. The name stems from the original mathematical models of the noise in old electronic devices, which was compared to shot (used in guns) hitting a surface.

In this post I will present a result known as Campbell’s theorem or Campbell’s formula, which gives the expectation of shot noise as as simple integral expression. This both a general holding for all point processes. It is also useful, as shot noise naturally arises in mathematical models. One application is wireless network models, where the interference term is shot noise.

But to present the main result, I first need to give some basics of point processes, most of which I already covered in this post.

Point process basics

We consider a point processes $\Phi$ defined on some underlying mathematical space $\mathbb{S}$, which is often $\mathbb{R}^n$. Researchers typically interpret a point process as a random counting measure, resulting in the use of integral and measure theory notation. For example, $\Phi(B)$ denotes the number of points located in some (Borel measurable) set $B$, which is a subset of $\mathbb{S}$.

For point processes, researchers often use a dual notation such that $\Phi$ denotes both a random set or a random measure. Then we can write, for example, $\Phi=\{X_i\}_i$ to stress that $\Phi$ is a random sets of points. (Strictly speaking, you can only use the set notation if the point process is simple, meaning that no two points coincide with probability one.)

The first moment measure of a point process, also called the mean measure or intensity measure, is defined as

$$\Lambda(B)= \mathbb{E} [\Phi(B)]. $$

In other words, the first moment measure can be interpreted as the expected number of points of $\Phi$ falling inside the set $B \subseteq \mathbb{S}$.

Shot noise definition

We assume a point process $\Phi=\{X_i\}_i$ is defined on some space $\mathbb{S}$. We consider a non-negative function $f$ with the domain $\mathbb{S}$, so $f:\mathbb{S} \rightarrow [0,\infty)$. If the point process $\Phi$ is a simple, we can use set notation and define the shot noise as

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

More generally, the shot noise is defined as

$$
S= \int_{ \mathbb{S}} f(x) \Phi(dx)\,.
$$

(We recall that an integral is simply a more general type of sum, which is why the integral sign comes from the letter S.)

Campbell’s theorem

We now state Campbell’s theorem.

Campbell’s theorem says that for any point process $\Phi$ defined on a space $\mathbb{S}$ the following formula holds

$$
\mathbb{E}[ S] = \int_{ \mathbb{S}} f(x) \Lambda(dx)\,,
$$

where $\Lambda= \mathbb{E} [\Phi(B)]$ is the intensity measure of the point process $\Phi$.

Interpretation

The integral formula is just an application of Fubini’s theorem, as we have simply changed the order of integration. The formula holds for general processes because it is simply a result on first moments, so it is leveraging the linearity of sums and integrals, including the expectation operator. Put more simply, the sum of parts does equal the whole.

Some history

At the beginning of the 20th century, Norman R. Campbell studied shot noise in Britain and wrote two key papers. In one of these papers appears a version of the result we now called Campbell’s theorem or Campbell’s formula. Interestingly, Campbell was a physicist who credited his mathematical result to renown pure mathematician G. H. Hardy. Hardy claimed years later that, given he only researched pure mathematics, none of his work would lead to applications. But Hardy’s claim is simply not true due to this result, as well as his results in number theory, which are famously used in modern day cryptography.

Shot noise

Given a mathematical model based on a point process, a quantity of possible interest is the sum of some function applied to each point of the point process. This random sum is called shot noise, where the name comes from developing mathematical models of the noise measured in old electronic devices, which was likened to shot (used in guns) hitting a surface.

Researchers have long studied shot noise induced by a point process. One particularly application is wireless network models, in which the interference term is an example of shot noise. It is also possible to construct new point processes, called shot noise Cox point processes, by using based on the shot noise of some initial point process.

For such applications, we need a more formal definition of shot noise.

Definition

Shot noise of a point process

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 $f:\mathbb{S} \rightarrow [0,\infty)$. This function $f$ is called the response function.

Then the shot noise is defined as
$$
I= \sum_{X_i\in \Phi} f(X_i)\,.
$$

Shot noise of a marked point process

The previous definition of 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 response function $g:\mathbb{S}\times \mathbb{M} \rightarrow [0,\infty)$ , the shot noise is defined as
$$
I’= \sum_{(X_i, M_i)\in \Phi’} g(X_i,M_i)\,.
$$

Properties

Given a point process on a space, like the plane, at any point the shot noise is simply a random variable. If we consider a subset of the space, then shot noise forms a random field, where we recall that a random field is simply a collection of random variables indexed by some set. (By convention, the set tends to be Euclidean space or a manifold). The shot noise can also be considered as a random measure, for example
$$
I(B)= \sum_{X_i\in \Phi\cap B} f(X_i)\,,
$$
where $B\subseteq \mathbb{S}$. This makes sense as the point process $\Phi$ is an example of a random (counting) measure.

For Poisson point processes, researchers have studied resulting shot noise random variable or field. For example, given a homogeneous Poisson point process on $\mathbb{R}^d$, if the response function is a simple power-law $f(x)=|x|^{-\beta}$, where $\beta> d$ and $|x|$ denotes the Euclidean distance from the origin, then the resulting shot noise is alpha stable random variable with parameter $\alpha=d/\beta$.

For a general point process $\Phi$ with intensity measure $\Lambda$, the first moment of the shot noise is simply
$$
\mathbb{E}(I)= \int_{\mathbb{S}} f(x) \Lambda (dx) \,.
$$

This is a result of Campbell’s theorem or formula. A similar expression exists for the shot noise of a marked point process.

Some history

Shot noise has been studied for over a century in science. In physics, Walter Schottky did research on shot noise in Germany at the beginning of the 20th century. In the same era, Norman R. Campbell studied shot noise in Britain and wrote two key papers, where one of them contains a result now called Campbell’s theorem or Campbell’s formula, among other names, which is a fundamental result in point process theory. Campbell was a physicist, but his work contains this mathematical result for which he credited the famed pure mathematician G. H. Hardy.

(It’s interesting to note that Hardy claimed years later that, given he did pure mathematics, none of his work would lead to applications, but that claim is simply not true for this and other reasons.)

The work on the physical process of shot noise motivated more probability-oriented papers on shot noise, including:

1944, S. O. Rice, Mathematical Analysis of Random Noise;
1960, Gilbert and Pollak, Amplitude distribution of shot noise;
1971, Daley, The definition of a multi-dimensional generalization of shot noise;
1977, J. Rice, On generalized shot noise;
1990 Lowen and Teich, Power-law shot noise.

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
$$
\text{SIR}(x,X_i) :=\frac{P_i(x)}{\sum\limits_{j\neq i} P_j(x)} =\frac{P_i(x)}{\sum\limits_{j=1}^{n} P_j(x)-P_i(x)} .
$$

The numerator is the signal and the denominator is the interference. This ratio tells us that 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.

Some history

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.

Definition

Over-dispersion

Special cases

Mixed Poisson point process

Log-Gaussian Cox point process

Cox-Poisson line-point process

Shot-noise Cox point process

Simulation

Further reading

Laplace functional

Name

Characterization

Poisson example

Applications

Proof techniques

Interference in wireless network models

Related functionals

Characteristic functional

Probability-generating functional

Further reading

Point process basics

Shot noise definition

Campbell’s theorem

Interpretation

Some history

Further reading

Definition

Shot noise of a point process

Shot noise of a marked point process

Properties

Some history

Further reading

Definition

Mathematical models

Propagation

Path loss

Fading and shadowing

Transmitters locations

Some history

Further reading