课程名称︰通讯随机过程 课程性质︰选修 课程教师︰钟嘉德 开课学院：电资 开课系所︰电信所 电机所 考试日期（年月日）︰ 考试时限（分钟）：3 HR 是否需发放奖励金：是 试题 : 1. Determine whether each of following statement is true or false. If the statement is true, prove it. If the statement is false, give a counterexample or explanation. Corrent choice without any proof, counterexample or explanation is not acceptable. (a) If two events A and B are mutually exclusive(i.e. their intersection is an empty set), then A and B are independent. (b) If the probability of an event A is equal to one, then A must be the universe space. (c) If three random variables X(μ), Y(μ) and Z(μ) are independent in pairs (i.e. any two of them are statistical independent), then they are mutually independent. (d) The function R(τ)= |τ|exp(-|τ|) can be the autocorrelation function od a wide-sense stationary random process (e) The function S(ω)= exp[ω +2ω^2 -ω^4] can be the power spetrum of a wide-sense stationary random process. (f) If a wide-sense stationary random process X(μ,t) has nonzero delta spetrum on ω=0, then the mean of X(μ,t) must be nonzero. (g) If X(μ,t) is a real-valued wide-sense stationary Gaussian random process with mean η_x = 1 and the autocorrelation function Rx(τ)= sin(τ)/τ + 1 ,then the random variable X(μ,t0) and X(μ,t0 +π) are independent for any fixed t0. (h) If the input to a linear and time-invariant system is a Gaussian random process, then the output is a stationary Gaussian random process. 2. Consider the experiment of rolling two fair dice independently. Define two random variable X(μ) and Y(μ) as the value of both dice that face upward after a single trail. Also, define the random variable Z(μ)= X(μ)+Y(μ) and W(μ)=X(μ)Y(μ). (a) Determine the probability Pr(Z(μ)=n) for all integer n. (b) Determine the conditional probability Pr(Z(μ)=n|X(μ)=m) for all integers n and m (c) Determine the mean E(W(μ)) (d) Determine the variance Var(W(μ)) 3. Define the real-valued random process X(μ,t) by X(μ,t) = R(μ)cos(ωt +ψ(μ)) where R(μ) and ψ(μ) are independent random variable, E(R^2(μ)) < ∞, and ψ(μ) is uniformly distributed in the interval (0,2π). Derive the mean function and the autocorrelation function of X(μ,t), and determine whether X(μ,t) is wide-sense stationary or not. 4. Consider a linear and time-invariant system with impluse response h(t), input process X(μ,t) and output process Y(μ,t). Show that if h(t)=0 outside the time interval (0,T) and X(μ,t) is zero-mean white noise, then Ry(t1,t2)=0 for |t1-t2|>T. 5. Consider independent and identically distributed random variable X1(μ), X2(μ),....Xn(μ) which are marginally uniform in the interval (0,1). Show that if Y(μ)=max(X1(μ),X2(μ),...,Xn(μ)), then Fy(y)=y^n for 0<y<1, Fy(y)=0 for y<0 and Fy(y)=1 for y>1. 6. Consider the wide-sense stationary random process X(μ,t) and Y(μ,t) which are related by Y(μ,t)= X(μ,t+1) -X(μ,t-1). Express (a) Ry(τ) in terms of Rx(τ) and (b) Sy(ω) in terms of Sx(ω). 7. Suppose that X1(μ) and X2(μ) are independent Poisson random variables with parameters λ1 and λ2, i.e Pr(Xi(μ)=k) = exp(-λi)(λi)^k / k! , if k is a nonnegative integer 0, otherwise for i=1,2. Define a new random variable Y(μ)= X1(μ) +X2(μ). Prove that Y(μ) is a Poisson random variable with parameter λ1+λ2. 8. Consider the real-valued stationary Gaussian random process X(μ,t) which has mean zero and autocorrelation Rx(τ)= exp(-|τ|)˙cos(τ). Also let Y(μ,t) = X^2(μ,t). Derive the autocorrealtion function of Y(μ,t). --

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