Lecture 2 Probability Review and Random Process

1. Lecture 2Probability Review and Random Process

2. Review of last lecture • The point worth noting are : • The source coding algorithm plays an important role in higher code rate (compressing data) • The channel encoder introduce redundancy in data • The modulation scheme plays important role in deciding the data rate and immunity of signal towards the errors introduced by the channel • Channel can introduce many types of errors due to thermal noise etc. • The demodulator and decoder should provide high Bit Error Rate (BER).

3. Review:Layering of Source Coding • Source coding includes • Sampling • Quantization • Symbols to bits • Compression • Decoding includes • Decompression • Bits to symbols • Symbols to sequence of numbers • Sequence to waveform (Reconstruction)

4. Review:Layering of Source Coding

5. Review:Layering of Channel Coding • Channel Coding is divided into • Discrete encoder\Decoder • Used to correct channel Errors • Modulation\Demodulation • Used to map bits to waveform for transmission

6. Review:Layering of Channel Coding

7. Review:Resources of a Communication System • Transmitted Power • Average power of the transmitted signal • Bandwidth (spectrum) • Band of frequencies allocated for the signal • Type of Communication system • Power limited System • Space communication links • Band limited Systems • Telephone systems

8. Review:Digital communication system • Important features of a DCS: • Transmitter sends a waveform from a finite set of possible waveforms during a limited time • Channel distorts, attenuates the transmitted signal and adds noise to it. • Receiver decides which waveform was transmitted from the noisy received signal • Probability of erroneous decision is an important measure for the system performance

9. Review of Probability

10. Sample Space and Probability • Random experiment: its outcome, for some reason, cannot be predicted with certainty. • Examples: throwing a die, flipping a coin and drawing a card from a deck. • Sample space: the set of all possible outcomes, denoted by S. Outcomes are denoted by E’s and each E lies in S, i.e., E ∈ S. • A sample space can be discrete or continuous. • Events are subsets of the sample space for which measures of their occurrences, called probabilities, can be defined or determined.

11. Three Axioms of Probability • For a discrete sample space S, define a probability measure P on as a set function that assigns nonnegative values to all events, denoted by E, in such that the following conditions are satisfied • Axiom 1: 0 ≤ P(E) ≤ 1 for all E ∈ S • Axiom 2: P(S) = 1 (when an experiment is conducted there has to be an outcome). • Axiom 3: For mutually exclusive events E1, E2, E3,. . . we have

12. Conditional Probability • We observe or are told that event E1 has occurred but are actually interested in event E2: Knowledge that of E1 has occurred changes the probability of E2 occurring. • If it was P(E2) before, it now becomes P(E2|E1), the probability of E2 occurring given that event E1 has occurred. • This conditional probability is given by • If P(E2|E1) = P(E2), or P(E2 ∩ E1) = P(E1)P(E2), then E1 and E2 are said to be statistically independent. • Bayes’ rule • P(E2|E1) = P(E1|E2)P(E2)/P(E1)

13. Mathematical Model for Signals • Mathematical models for representing signals • Deterministic • Stochastic • Deterministic signal: No uncertainty with respect to the signal value at any time. • Deterministic signals or waveforms are modeled by explicit mathematical expressions, such as x(t) = 5 cos(10*t). • Inappropriate for real-world problems??? • Stochastic/Random signal: Some degree of uncertainty in signal values before it actually occurs. • For a random waveform it is not possible to write such an explicit expression. • Random waveform/ random process, may exhibit certain regularities that can be described in terms of probabilities and statistical averages. • e.g. thermal noise in electronic circuits due to the random movement of electrons

14. Energy and Power Signals • The performance of a communication system depends on the received signal energy: higher energy signals are detected more reliably (with fewer errors) than are lower energy signals. • An electrical signal can be represented as a voltage v(t) or a current i(t) with instantaneous power p(t) across a resistor defined by OR

15. Energy and Power Signals • In communication systems, power is often normalized by assuming R to be 1. • The normalization convention allows us to express the instantaneous power as where x(t) is either a voltage or a current signal. • The energy dissipated during the time interval (-T/2, T/2) by a real signal with instantaneous power expressed by Equation (1.4) can then be written as: • The average power dissipated by the signal during the interval is:

16. Energy and Power Signals • We classify x(t) as an energy signal if, and only if, it has nonzero but finite energy (0 < Ex< ∞) for all time, where • An energy signal has finite energy but zero average power • Signals that are both deterministic and non-periodic are termed as Energy Signals

17. Energy and Power Signals • Power is the rate at which the energy is delivered • We classify x(t) as an power signal if, and only if, it has nonzero but finite energy (0 < Px< ∞) for all time, where • A power signal has finite power but infinite energy • Signals that are random or periodic termed as Power Signals

18. Random Variable • Functions whose domain is a sample space and whose range is a some set of real numbers is called random variables. • Type of RV’s • Discrete • E.g. outcomes of flipping a coin etc • Continuous • E.g. amplitude of a noise voltage at a particular instant of time

19. Random Variables Random Variables • All useful signals are random, i.e. the receiver does not know a priori what wave form is going to be sent by the transmitter • Let a random variable X(A) represent the functional relationship between a random event A and a real number. • The distribution function Fx(x) of the random variable X is given by

20. Random Variable • A random variable is a mapping from the sample space to the set of real numbers. • We shall denote random variables by boldface, i.e., x, y, etc., while individual or specific values of the mapping x are denoted by x(w).

21. Real number time (t) Random process • A random process is a collection of time functions, or signals, corresponding to various outcomes of a random experiment. For each outcome, there exists a deterministic function, which is called a sample function or a realization. Random variables Sample functions or realizations (deterministic function)

22. Random Process • A mapping from a sample space to a set of time functions.

23. Random Process contd • Ensemble: The set of possible time functions that one sees. • Denote this set by x(t), where the time functions x1(t, w1), x2(t, w2), x3(t, w3), . . . are specific members of the ensemble. • At any time instant, t = tk, we have random variable x(tk). • At any two time instants, say t1 and t2, we have two different random variables x(t1) and x(t2). • Any realationship b/w any two random variables is called Joint PDF

24. Classification of Random Processes • Based on whether its statistics change with time: the process is non-stationary or stationary. • Different levels of stationary: • Strictly stationary: the joint pdf of any order is independent of a shift in time. • Nth-order stationary: the joint pdf does not depend on the time shift, but depends on time spacing

25. Cumulative Distribution Function (cdf) • cdf gives a complete description of the random variable. It is defined as: FX(x) = P(E ∈ S : X(E) ≤ x) = P(X ≤ x). • The cdf has the following properties: • 0 ≤ FX(x) ≤ 1 (this follows from Axiom 1 of the probability measure). • Fx(x) is non-decreasing: Fx(x1) ≤ Fx(x2) if x1 ≤ x2 (this is because event x(E) ≤ x1 is contained in event x(E) ≤ x2). • Fx(−∞) = 0 and Fx(+∞) = 1 (x(E) ≤ −∞ is the empty set, hence an impossible event, while x(E) ≤ ∞ is the whole sample space, i.e., a certain event). • P(a < x ≤ b) = Fx(b) − Fx(a).

26. Probability Density Function • The pdf is defined as the derivative of the cdf: fx(x) = d/dx Fx(x) • It follows that: • Note that, for all i, one has pi ≥ 0 and ∑pi = 1.

27. Cumulative Joint PDF Joint PDF • Often encountered when dealing with combined experiments or repeated trials of a single experiment. • Multiple random variables are basically multidimensional functions defined on a sample space of a combined experiment. • Experiment 1 • S1 = {x1, x2, …,xm} • Experiment 2 • S2 = {y1, y2 , …, yn} • If we take any one element from S1 and S2 • 0 <= P(xi, yj) <= 1 (Joint Probability of two or more outcomes) • Marginal probabilty distributions • Sum all j P(xi, yj) = P(xi) • Sum all i P(xi, yj) = P(yi)

28. Expectation of Random Variables(Statistical averages) • Statistical averages, or moments, play an important role in the characterization of the random variable. • The first moment of the probability distribution of a random variable X is called mean value mx or expected value of a random variable X • The second moment of a probability distribution is mean-square value of X • Central moments are the moments of the difference between X and mx, and second central moment is the variance of x. • Variance is equal to the difference between the mean-square value and the square of the mean

29. Contd • The variance provides a measure of the variable’s “randomness”. • The mean and variance of a random variable give a partial description of its pdf.

30. Time Averaging and Ergodicity • A process where any member of the ensemble exhibits the same statistical behavior as that of the whole ensemble. • For an ergodic process: To measure various statistical averages, it is sufficient to look at only one realization of the process and find the corresponding time average. • For a process to be ergodic it must be stationary. The converse is not true.

31. Gaussian (or Normal) Random Variable (Process) • A continuous random variable whose pdf is: μ and are parameters. Usually denoted as N(μ, ) . • Most important and frequently encountered random variable in communications.

32. Central Limit Theorem • CLT provides justification for using Gaussian Process as a model based if • The random variables are statistically independent • The random variables have probability with same mean and variance

33. CLT • The central limit theorem states that • “The probability distribution of Vn approaches a normalized Gaussian Distribution N(0, 1) in the limit as the number of random variables approach infinity” • At times when N is finite it may provide a poor approximation of for the actual probability distribution

34. Autocorrelation Autocorrelation of Energy Signals • Correlation is a matching process; autocorrelation refers to the matching of a signal with a delayed version of itself • The autocorrelation function of a real-valued energy signal x(t) is defined as: • The autocorrelation function Rx() provides a measure of how closely the signal matches a copy of itself as the copy is shifted  units in time. • Rx()is not a function of time; it is only a function of the time difference  between the waveform and its shifted copy.

35. Autocorrelation • symmetrical in  about zero • maximum value occurs at the origin • autocorrelation and ESD form a Fourier transform pair, as designated by the double-headed arrows • value at the origin is equal to the energy of the signal

36. AUTOCORRELATION OF A PERIODIC (POWER) SIGNAL • The autocorrelation function of a real-valued power signal x(t) is defined as: • When the power signal x(t) is periodic with period T0, the autocorrelation function can be expressed as:

37. Autocorrelation of power signals • symmetrical in  about zero • maximum value occurs at the origin • autocorrelation and PSD form a Fourier transform pair, as designated by the double-headed arrows • value at the origin is equal to the average power of the signal The autocorrelation function of a real-valued periodic signal has properties similar to those of an energy signal:

40. Spectral Density

41. SPECTRAL DENSITY • The spectral density of a signal characterizes the distribution of the signal’s energy or power, in the frequency domain • This concept is particularly important when considering filtering in communication systems while evaluating the signal and noise at the filter output. • The energy spectral density (ESD) or the power spectral density (PSD) is used in the evaluation. • Need to determine how the average power or energy of the process is distributed in frequency.

42. Spectral Density • Taking the Fourier transform of the random process does not work

43. ENERGY SPECTRAL DENSITY • Energy spectral density describes the energy per unit bandwidth measured in joules/hertz • Represented as x(t), the squared magnitude spectrum x(t) =|x(f)|2 • According to Parseval’s Relation • Therefore • The Energy spectral density is symmetrical in frequency about origin and total energy of the signal x(t) can be expressed as

44. Power Spectral Density • The power spectral density (PSD) function Gx(f) of the periodic signal x(t) is a real, even ad nonnegative function of frequency that gives the distribution of the power of x(t) in the frequency domain. • PSD is represented as (Fourier Series): • PSD of non-periodic signals: • Whereas the average power of a periodic signal x(t) is represented as:

45. Noise

46. Noise in the Communication System • The term noise refers to unwanted electrical signals that are always present in electrical systems: e.g. spark-plug ignition noise, switching transients and other electro-magnetic signals or atmosphere: the sun and other galactic sources • Can describe thermal noise as zero-mean Gaussian random process • A Gaussian process n(t) is a random function whose value n at any arbitrary time t is statistically characterized by the Gaussian probability density function

47. WHITE NOISE • The primary spectral characteristic of thermal noise is that its power spectral density is the same for all frequencies of interest in most communication systems • A thermal noise source emanates an equal amount of noise power per unit bandwidth at all frequencies—from dc to about 1012 Hz. • Power spectral density G(f) • Autocorrelation function of white noise is • The average power P of white noise if infinite

48. White Noise

49. White Noise • Since Rw( T) = 0 for T = 0, any two different samples of white noise, no matter how close in time they are taken, are uncorrelated. • Since the noise samples of white noise are uncorrelated, if the noise is both white and Gaussian (for example, thermal noise) then the noise samples are also independent.

50. Additive White Gaussian Noise (AWGN) • The effect on the detection process of a channel with Additive White Gaussian Noise (AWGN) is that the noise affects each transmitted symbol independently • Such a channel is called a memoryless channel • The term “additive” means that the noise is simply superimposed or added to the signal—that there are no multiplicative mechanisms at work