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.*

## Further reading

As a model for interference in wireless networks, shot noise is covered in books such as 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.