The focus of this course is on digital communication, which involves transmission of information, in its most general sense, from source to destination using digital technology. Engineering such a system requires modeling both the information and the transmission media. Interestingly, modeling both digital or analog information and many physical media requires a probabilistic setting. In this chapter and in the next one we will review the theory of probability, model random signals, and characterize their behavior as they traverse through deterministic systems disturbed by noise and interference. In order to develop practical models for random phenomena we start with carrying out a random experiment. We then introduce definitions, rules, and axioms for modeling within the context of the experiment. The outcome of a random experiment is denoted by ω. The sample space Ω is the set of all possible outcomes of a random experiment. Such outcomes could be an abstract description in words. A scientific experiment should indeed be repeatable where each outcome could naturally have an associated probability of occurrence. This is defined formally as the ratio of the number of times the outcome occurs to the total number of times the experiment is repeated.

Random Variables A random variable is the assignment of a real number to each outcome of a random experiment.

Roll a dice. Outcomes ω 1 ω 2 ω 3 ω 4 ω 5 ω 6 ω i = i dots on the face of the dice. X ω i i

Distributions Probability assignments on intervals a X b Cumulative distribution The cumulative distribution function of a random variable X is a function F X ↦ such that F X b X b ω Ω X ω b

Continuous Random Variable A random variable X is continuous if the cumulative distribution function can be written in an integral form, or F X b x b f X x and f X x is the probability density function (pdf) (e.g., F X x is differentiable and f X x x F X x ) Discrete Random Variable A random variable X is discrete if it only takes at most countably many points (i.e., F X · is piecewise constant). The probability mass function (pmf) is defined as p X x k X x k F X x k x x x k x x k F X x Two random variables defined on an experiment have joint distribution F X Y a b X a Y b ω Ω X ω a Y ω b

Joint pdf can be obtained if they are jointly continuous F X Y a b y b x a f X Y x y (e.g., f X Y x y x y F X Y x y ) Joint pmf if they are jointly discrete p X Y x k y l X x k Y y l Conditional density function f Y | X y | x f X Y x y f X x for all x with f X x 0 otherwise conditional density is not defined for those values of x with f X x 0 Two random variables are independent if f X Y x y f X x f Y y for all x and y . For discrete random variables, p X Y x k y l p X x k p Y y l for all k and l.

Moments Statistical quantities to represent some of the characteristics of a random variable. g X g X x g x f X x continuous k k g x k p X x k discrete Mean μ X X Second moment X 2 X 2 Variance Var X X X μ X 2 X 2 μ X 2 Characteristic function Φ X u u X for u , where 1 Correlation between two random variables R X Y X Y * y x x y * f X Y x y X and Y are jointly continuous k k l l x k y l * p X Y x k y l X and Y are jointly discrete Covariance C X Y Cov X Y X μ X Y μ Y * R X Y μ X μ Y * Correlation coefficient ρ X Y Cov X Y σ X σ Y Uncorrelated random variables Two random variables X and Y are uncorrelated if ρ X Y 0 .