AWGs have always been a great way to create serial data or clock signals as you have the ability to produce accurate signals with very precise edge placements. Usually you can place an edge crossing to better than 1/100th of the sample interval of the AWG. With this accuracy you are able to add sub-nanosecond timing error to clock or data signals to test your systems susceptibility to jitter. With basic AWG waveform memories now in the millions of points, you are now able to add jitter to longer pulse patterns in more interesting ways.
Jitter is defined as the deviation in or displacement of some aspect of the pulses in a digital signal. What is most often characterized is the Time Interval Error (TIE) which is the timing deviation of the edge crossing of the serial data signal relative to a clock. This can also be the timing deviation of a clock relative to an ideal clock.
Jitter and TIE can end up on a signal through a variety of mechanisms. For example, jitter can be the result of spurious coupling from a switching power supply to the digital systems clock signal. A designer would be prudent to test his systems vulnerability to such an occurrence. While there are many more expensive solutions for injecting jitter onto a clock signal, most AWGs are perfectly capable of simulating this kind of jitter.
You can change the edge crossing position of an arbitrary waveform very precisely in steps on the order of the sample period of the AWG (1/sample rate) divided by its vertical resolution in bits. You also want to pay close attention to the AWG's jitter spec, which specifies the amount of jitter error that the AWG will add to your "ideal" digital signal. Modern AWGs will have jitter specs < 100 ps, for instance Agilent's 33521/22A AWGs have a jitter spec of < 40 ps. Now we need a mathematical algorithm that allows us to easily create our digital pulses and allows us to easily manipulate the pulse edges to simulate jitter. Lets first start out with creating the pulses using the error function (ERF), which is the integral of the Gaussian or Normal Distribution. The ERF is defined and plotted as (click to enlarge):