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COMMUNICATION SYSTEMS BY Mr.V.Sudheer Raja, M.Tech Assistant professor, Department of Electrical Engineering Adama Science and Technology University

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CHAPTER I Review of Probability, Random Variables and Random Process Contents : Introduction Definition of Probability, Axioms of Probability Sub-topics of Probability Random Variable and its Advantages Probability Mass function & Probability density function Conditional, Joint, Bernoulli and Binomial Probabilities Random Process Mr V Sudheer Raja ASTU

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Introduction Models of a Physical Situation: A model is an approximate representation. --Mathematical model --Simulation models Deterministic versus Stochastic/Random -- Deterministic models offer repeatability of measurements Ex: Ohm’s Laws, model of a capacitor/inductor/resistor -- Stochastic models don’t: Ex: Processor’s caching, queuing, and estimation of task execution time The emphasis of this course would be on Stochastic Modeling. Mr V Sudheer Raja ASTU

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Probability In any communication system the signals encountered may be of two types: -- Deterministic and Random. Deterministic signals are the class of the signals that may be predicted at any instant of time, and can be modeled as completely specified functions of time. Random signals, it is not possible to predict its precise value in advance. It is possible to described these signals in terms of its statistical properties such as the average power in the random signal, or the spectral distribution of the power. The mathematical discipline that deals with the statistical characterization of random signals is probability theory. Mr V Sudheer Raja ASTU

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Probability Theory The phenomenon of statistical regularity is used to explain probability. Probability theory is rooted in phenomena that, explicitly or implicitly, can be modeled by an experiment or situation with an outcome or result respectively that is subject to a chance. If the experiment is repeated the outcome may differ because of underlying random phenomena or chance mechanism. Such an experiment is referred to as random experiment. To use the idea of statistical regularity to explain the concept of probability, we proceed as follows: 1. We prescribe a basic experiment, which is random in nature and is repeated under identical conditions. 2. We specify all the possible outcomes of the experiment. As the experiment is random in nature, on any trial of the experiment the above possible outcomes are unpredictable i.e. any of the outcome prescribed may be resulted. 3. For a large number of trials of the experiment, the outcomes exhibit statistical regularity; that is a definite average pattern of outcomes is observed if the experiment is repeated a large number of times. Mr V Sudheer Raja ASTU

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Random experiment Random Experiment : The outcome of the experiment varies in an random manner The possible outcome of an experiment is termed as event --Ex: In case of coin tossing experiment, the possibility of occurrence of “head” or “tail” is treated as event. Sample Space: --The set of possible results or outcomes of an experiment i.e. totality of all events without repetition is called as sample space and denoted as ‘S’. --Ex : In case of coin tossing experiment, the possible outcomes are either “head” or “tail”,thus the sample space can be defined as, S={head, tail} Mr V Sudheer Raja ASTU

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Sample Spaces for some example experiments 1. Select a ball from an urn containing balls numbered 1 to Select a ball from an urn containing balls numbered 1 to 4. Balls 1 and 2 are black, and 3 and 4 are white. Note the number and the color of the ball 3. Toss a coin three times and note the number of heads 4. Pick a number at random between 0 and 1 5. Measure the time between two message arrivals at a messaging center 6. Pick 2 numbers at random between 0 and 1 7. Pick a number X at random between zero and one, then pick a number Y at random between 0 and X. Mr V Sudheer Raja ASTU

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Some events in the experiments on the previous slide 1. An even numbered ball is selected 2. The ball is white and even-numbered 3. Each toss is the same outcome 4. The number selected is non-negative 5. Less than 5 seconds elapse between message arrivals 6. Both numbers are less than The two numbers differ by less than one- tenth Null event, elementary event, certain event Mr V Sudheer Raja ASTU

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The urn experiment : Questions? Consider an urn with three balls in it, labeled 1,2,3 What are the chances that a ball withdrawn at random from the urn is labeled ‘1’? How to quantify this ‘chance’? Is withdrawing any of the three balls equally likely (equi-probable); or if any ball is more likely to be drawn compared to the others? If someone assigns that ‘1’ means ‘sure occurrence’ and ‘0’ means ‘no chance of occurrence’, then what number would you give to the chance of getting ‘ball 1’? And how do you compare the chance of withdrawing an odd-numbered ball to that of withdrawing an even- numbered ball? Mr V Sudheer Raja ASTU

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Counts of the selections of ‘kth’ outcome in ‘n’ iterations (trails) of the random experiment is given by N k (n) The ratio N k (n)/n is called the relative frequency of ‘kth’ outcome is defined as: If ‘kth’ outcome occurs in none of the trails then N k (n)=0 i.e., N k (n)/n = 0 and if ‘kth’ event occurs as outcome for all the trails then N k (n)=n i.e., N k (n)/n =1.Clearly the relative frequency is a nonnegative real number less than or equal to 1 i.e., 0≤ N k (n)/n ≤ 1 Mr V Sudheer Raja ASTU

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Inferences Statistical regularity: --Long-term averages of repeated iterations of a random experiment tend to yield the same value A few ideas of note: And regarding the chances of withdrawing an odd-numbered ball, Mr V Sudheer Raja ASTU

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Axioms of Probability A probability system consists of the following triple: 1.A sample space ’S’ of elementary events(outcomes) 2.A class of events that are subset of ‘S’ 3.A probability measure P(.) assigned to each event (say ‘A’) in the class, which has the following properties: (i) P(S)=1, The probability of sure event is1 (ii) 0≤ P(A) ≤ 1, The probability of an event A is a non negative real number that is less than or equal to 1 (iii) If A and B are two mutually exclusive events in the given class then, P(A+B)= P(A)+ P(B) Mr V Sudheer Raja ASTU

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Three axioms are used to define the probability and are also used to define some other properties of the probability. Property 1 P(Ā)=1- P(A) where Ā is the complement of event ‘A’ Property 2 -- If M mutually exclusive A 1, A 2, A M have the exhaustive property A 1 + A A M = S Then P(A 1 ) + P(A 2 ) + P(A 3 ) P(A M )=1 Property 3 -- When events A and B are not mutually exclusive events then the probability of union event “A or B” equals P(A+B)= P(A)+ P(B) - P(AB) Where P(AB) is called a joint probability --Joint probability has the following relative frequency interpretation, P(AB)= Mr V Sudheer Raja ASTU

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Conditional Probability If an experiment involves two events A and B. Let P(B/A) denotes the probability of event B, given that event A has occurred. The probability P(B/A) is called the Conditional probability of B given A. Assuming that A has nonzero probability, the conditional probability P(B/A) is defined as, P(B/A)=P(AB)/P(A), Where P(AB) is the joint probability of A and B. P(B/A)=,where represents the relative frequency of B given A has occurred. The joint probability of two events may be expressed as the product of conditional probability of one event given the other times the elementary probability of the other. that is, P(AB)=P(B/A)* P(A) = P(A/B)*P(B) Mr V Sudheer Raja ASTU

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Random Variables A function whose domain is a sample space and whose range is some set of real numbers is called a random variable of the experiment. Random variable is denoted as X(s) or simply X, where ‘s’ is called sample point corresponds to an outcome that belongs to sample space ‘S’. Random variables may be discrete or continuous The random variable X is a discrete random variable if X can take on only a finite number of values in any finite observation interval. Ex: X(k)=k, where ‘k’ is sample point with the event showing k dots when a die is thrown, where it has limited number of possibilities like either 1 or 2 or 3 or 4 or 5 or 6 dots to show. If X can take on any value in a whole observation interval, X is called Continuous random variable. Ex: Random variable representing amplitude of a noise voltage at a particular instant of time because it may take any value between plus and minus infinity. Mr V Sudheer Raja ASTU

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For probabilistic description of random variable, let us consider the random variable X and the probability of the event X ≤ x, denoted by P(X ≤ x ) i.e. the probability is the function of the dummy variable x which can be expressed as, F X ( x )= P(X ≤ x ) The function F X ( x ) is called the cumulative distribution function or distribution function of the random variable X. and it has the following properties, 1. The distribution function F X ( x ) is bounded between zero and one. 2. The distribution function F X ( x ) is a monotone non- decreasing function of x, i.e. F X ( x 1 )≤ F X ( x 2 ), if x 1 < x 2 Mr V Sudheer Raja ASTU

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Probability density function -- The derivative of distribution function is called Probability density function. i.e Eq.(1). --Differentiation in Eq.(1) is with respect to the dummy variable x and the name density function arises from the fact that the probability of the event x 1 < X ≤ x 2 equals P( x 1 < X ≤ x 2 ) = P(X ≤ x 2 )-P(X ≤ x 1 ) = F X ( x 2 )- F X ( x 1 ) = Eq.(2) Since F X (∞)=1,Corresponding to the probability of certain event and F X (-∞)=0 corresponds to the probability of an impossible event. which follows immediately that i.e. the probability density function must always be a nonnegative function and with a total area of one. Mr V Sudheer Raja ASTU

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Statistical Averages The mean value or the expected value of a random variable is defined as, m X = where E denotes the expectation operator, that is the mean value m X locates the center of gravity of the area under the probability density function curve of the random variable X. Mr V Sudheer Raja ASTU

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The variance of the random variable X is the measure of the variables randomness, it constrains the effective width of the probability density function f X ( x ) of the random variable X about the mean m X and is expressed as, The variance of random variable is normally denoted as The square root of Variance is called as standard deviation of the random variable X. Mr V Sudheer Raja ASTU

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Random Processes Description of Random Processes Stationary and ergodicty Autocorrelation of Random Processes Cross-correlation of Random Processes

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Random Processes A RANDOM VARIABLE X, is a rule for assigning to every outcome, of an experiment a number X( . Note: X denotes a random variable and X( denotes a particular value. A RANDOM PROCESS X(t) is a rule for assigning to every a function X(t, Note: for notational simplicity we often omit the dependence on . Mr V Sudheer Raja ASTU

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Ensemble of Sample Functions The set of all possible functions is called the ENSEMBLE. Mr V Sudheer Raja ASTU

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Random Processes A general Random or Stochastic Process can be described as: Collection of time functions (signals) corresponding to various outcomes of random experiments. Collection of random variables observed at different times. Examples of random processes in communications: Channel noise, Information generated by a source, Interference. t1t1 t2t2 Mr V Sudheer Raja ASTU

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Collection of Time Functions Consider the time-varying function representing a random process where i represents an outcome of a random event. Example: a box has infinitely many resistors (i=1,2,...) of same resistance R. Let i be event that the ith resistor has been picked up from the box Let v(t, i ) represent the voltage of the thermal noise measured on this resistor. Mr V Sudheer Raja ASTU

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Collection of Random Variables For a particular time t=t o the value x(t o, i is a random variable. To describe a random process we can use collection of random variables {x(t o, 1 , x(t o, 2 , x(t o, 3 ,... }. Type: a random processes can be either discrete-time or continuous-time. Ex:Probability of obtaining a sample function of a RP that passes through the following set of windows. Probability of a joint event. Mr V Sudheer Raja ASTU

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Description of Random Processes Analytical description: X(t) =f(t, ) where is an outcome of a random event. Statistical description: For any integer N and any choice of (t 1, t 2,..., t N ) the joint pdf of {X(t 1 ), X( t 2 ),..., X( t N ) } is known. To describe the random process completely the PDF f(x) is required. Mr V Sudheer Raja ASTU

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Activity: Ensembles Consider the random process: x(t)=At+B Draw ensembles of the waveforms: B is constant, A is uniformly distributed between [- 1,1] A is constant, B is uniformly distributed between [0,2] Does having an “Ensemble” of waveforms give you a better picture of how the system performs? B x(t)x(t) t Slope Random 2 x(t) t B intersect is Random Mr V Sudheer Raja ASTU

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Stationarity Definition: A random process is STATIONARY to the order N if for any t 1,t 2,..., t N, f x {x(t 1 ), x(t 2 ),...x(t N )}=f x {x(t 1 +t 0 ), x(t 2 +t 0 ),...,x(t N +t 0 )} This means that the process behaves similarly (follows the same PDF) regardless of when you measure it. A random process is said to be STRICTLY STATIONARY if it is stationary to the order of N→∞. Is the random process from the coin tossing experiment stationary? Mr V Sudheer Raja ASTU

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Illustration of Stationarity Time functions pass through the corresponding windows at different times with the same probability. Mr V Sudheer Raja ASTU

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Example of First-Order Stationarity Assume that A and 0 are constants; 0 is a uniformly distributed RV from ) t is time. The PDF of given x(t): Note: there is NO dependence on time, the PDF is not a function of t. The RP is STATIONARY. Mr V Sudheer Raja ASTU

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Non-Stationary Example Now assume that A, 0 and 0 are constants; t is time. Value of x(t) is always known for any time with a probability of 1. Thus the first order PDF of x(t) is Note: The PDF depends on time, so it is NONSTATIONARY. Mr V Sudheer Raja ASTU

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Ergodic Processes Definition: A random process is ERGODIC if all time averages of any sample function are equal to the corresponding ensemble averages (expectations) Example, for ergodic processes, can use ensemble statistics to compute DC values and RMS values Ergodic processes are always stationary; Stationary processes are not necessarily ergodic Mr V Sudheer Raja ASTU

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Example: Ergodic Process A and 0 are constants; 0 is a uniformly distributed RV from ) t is time. Mean (Ensemble statistics) Variance Mr V Sudheer Raja ASTU

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Example: Ergodic Process Mean (Time Average) T is large Variance The ensemble and time averages are the same, so the process is ERGODIC Mr V Sudheer Raja ASTU

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Autocorrelation of Random Process The Autocorrelation function of a real random process x(t) at two times is: Mr V Sudheer Raja ASTU

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Wide-sense Stationary A random process that is stationary to order 2 or greater is Wide-Sense Stationary: A random process is Wide-Sense Stationary if: Usually, t 1 =t and t 2 =t+ so that t 2 - t 1 = Wide-sense stationary process does not DRIFT with time. Autocorrelation depends only on the time gap but not where the time difference is. Autocorrelation gives idea about the frequency response of the RP. Mr V Sudheer Raja ASTU

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Cross Correlations of RP Cross Correlation of two RP x(t) and y(t) is defined similarly as: If x(t) and y(t) are Jointly Stationary processes, If the RP’s are jointly ERGODIC, Mr V Sudheer Raja ASTU

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