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Random Signals RANDOM SIGNALS Random Variables A random variable x(ξ) is a mapping that assigns a real number x to every outcome ξ from an abstract probability space. The mapping should satisfy the following two conditions: • the interval {x(ξ) ≤ x} is an event in the abstract probabilty space for every x; • Pr[x(ξ) < ∞] = 1 and Pr[x(ξ) = −∞] = 0. Cumulative distribution function (cdf) of a random variable x(ξ): Fx(x) = Pr{x(ξ) ≤ x}. Probability density function (pdf): dF (x) f (x) = x . x dx Then Z x Fx(x) = fx(x) dx. −∞ EE 524, Fall 2004, # 7 1 Since Fx(∞) = 1, we have normalization condition: Z ∞ fx(x)dx = 1. −∞ Several important properties: 0 ≤ Fx(x) ≤ 1,Fx(−∞) = 0,Fx(∞) = 1, Z ∞ fx(x) ≥ 0, fx(x) dx = 1. −∞ Simple interpretation: Pr{x − ∆/2 ≤ x(ξ) ≤ x + ∆/2} fx(x) = lim . ∆→0 ∆ EE 524, Fall 2004, # 7 2 Expectation of an arbitrary function g(x(ξ)): Z ∞ E {g(x(ξ))} = g(x)fx(x) dx. −∞ Mean: Z ∞ µx = E {x(ξ)} = xfx(x) dx. −∞ Variance of a real random variable x(ξ): 2 2 var{x} = σx = E {(x − E {x}) } = E {x2 − 2xE {x} + E {x}2} = E {x2} − (E {x})2 2 2 = E {x } − µx. Complex random variables: A complex random variable x(ξ) = xR(ξ) + jxI(ξ). Although the definition of the mean remains unchanged, the definition of variance changes for complex x(ξ): 2 2 var{x} = σx = E {|x − E {x}| } = E {|x|2 − xE {x}∗ − x∗E {x} + |E {x}|2} = E {|x|2} − |E {x}|2 2 2 = E {|x| } − |µx| . EE 524, Fall 2004, # 7 3 Random Vectors A real-valued vector containing N random variables x1(ξ) x2(ξ) x(ξ) = . . xN (ξ) is called a random N vector or a random vector when dimensionality is unimportant. A real-valued random vector ≡ mapping from an abstract probability space to a vector- valued real space RN . A random vector is completely characterized by its joint cumulative distribution function, which is defined by 4 Fx(x1, x2, . , xN ) = P [{x1(ξ) ≤ x1} ∩ ... ∩ {xN (ξ) ≤ xN }] and is often written as Fx(x) = P [x(ξ) ≤ x]. A random vector can also be characterized by its joint EE 524, Fall 2004, # 7 4 probability density function (pdf), defined as follows: fx(x) = lim ∆x1 → 0 ∆x2 → 0 . ∆xN → 0 P [{x1 < x1(ξ) ≤ x1 + ∆x1} ∩ ... ∩ {xN < xN (ξ) ≤ xN + ∆xN }] ∆x1 ··· ∆xN ∂ ∂ = ··· Fx(x). ∂x1 ∂xN The function Z Z fxi(xi) = ··· fx(x) dx1 ··· dxi−1 dxi+1 ··· dxN (N−1) is known as marginal pdf and describes individual random variables. The cdf of x can be computed from the joint pdf as: x x x Z 1 Z N 4 Z Fx(x) = ··· fx(v) dv1dv2 ··· dvN = fx(v) dv. −∞ −∞ −∞ EE 524, Fall 2004, # 7 5 Complex random vectors: xR,1(ξ) xI,1(ξ) xR,2(ξ) xI,2(ξ) x(ξ) = xR(ξ) + jxI(ξ) = . + j . . . . xR,N (ξ) xI,N (ξ) Complex random vector ≡ mapping from an abstract probability space to a vector-valued complex space CN . The cdf of a complex-valued random vector x(ξ) is defined as: 4 Fx(x) = P [x(ξ) ≤ x] 4 = P [{xR(ξ) ≤ xR} ∩ {xI(ξ) ≤ xI}] and its joint pdf is defined as fx(x) = lim ∆xR,1 → 0 ∆xI,1 → 0 . ∆xR,N → 0 ∆xI,N → 0 P [{xR < xR(ξ) ≤ xR + ∆xR} ∩ {xI < xI(ξ) ≤ xI + ∆xI}] ∆x1 ··· ∆xN ∂ ∂ ∂ ∂ = ··· Fx(x). ∂xR,1 ∂xI,1 ∂xR,N ∂xI,N EE 524, Fall 2004, # 7 6 The cdf of x can be computed from the joint pdf as: Z xR,1 Z xI,N Fx(x) = ··· fx(v) dvR,1dvI,1 ··· dvR,N dvI,N −∞ −∞ x 4 Z N = fx(v) dv, −∞ where the single integral in the last expression is used as a compact notation for a multidimensional integral and should not be confused with a complex contour integral. Note that Z xe Z x Fx(x) = fx(xe) dxe = fx(x) dx. −∞ −∞ T T T where xe = [xR, xI ] . T For two random variables, x = [x, y] : fx(x) = fx,y(x, y). x and y are independent if fx,y(x, y) = fx(x) · fy(y) =⇒ E {xy} = E {x}E {y}. Expectation of a function g(x(ξ)): Z ∞ E {g(x)} = g(x)fx(x)dx. −∞ EE 524, Fall 2004, # 7 7 For two random variables, x = [x, y]T : Z ∞ E {g(x, y)} = g(x, y)fx,y(x, y) dxdy. −∞ Correlation: Real correlation: Z ∞ Z ∞ rx,y = E {xy} = xyfx,y(x, y) dxdy. −∞ −∞ Real covariance: rx,y = E {(x − µx)(y − µy)} Z ∞ Z ∞ = (x − µx)(y − µy) fx,y(x, y) dxdy. −∞ −∞ Complex correlation: Z ∞ Z ∞ ∗ ∗ rx,y = E {xy } = xy fx,y(x, y) dxdy. −∞ −∞ Complex covariance: ∗ rx,y = E {(x − µx)(y − µy) } Z ∞ Z ∞ ∗ = (x − µx)(y − µy) fx,y(x, y) dxdy. −∞ −∞ EE 524, Fall 2004, # 7 8 Covariance Matrix: Mean vector: µx = E {x}. Real covariance matrix: T Rx = E {(x − E {x})(x − E {x}) } = E {xxT } − E {x}E {x}T , T Rx = E {xx } if E {x} = 0. Complex covariance matrix: H Rx = E {(x − E {x})(x − E {x}) } = E {xxH} − E {x}E {x}H, H Rx = E {xx } if E {x} = 0. Observe the following property of complex correlation: ∗ ∗ ∗ ∗ ri,k = E {xixk} = E {xkxi } = rk,i. EE 524, Fall 2004, # 7 9 Then, for E {x} = 0: r1,1 r1,2 ······ r1,N H r2,1 r2,2 ······ r2,N Rx = E {xx } = . . rN,1 rN,2 ······ rN,N r1,1 r1,2 ······ r1,N ∗ r1,2 r2,2 ······ r2,N = . . . ∗ ∗ r1,N r2,N ······ rN,N The covariance matrix is Hermitian. It is positive semidefinite because H H H b Rxb = b E {(x − E {x})(x − E {x}) }b | {zz } = bHE {zzH}b = E {|bHz|2} ≥ 0. Linear Transformation of Random Vectors Linear Transformation: y = g(x) = Ax. Mean Vector: µy = E {Ax} = Aµx. EE 524, Fall 2004, # 7 10 Covariance Matrix: H H Ry = E {yy } − µyµy H H H H = E {Axx A } − Aµxµx A H H H = A E {xx } − µxµx A H = ARxxA . EE 524, Fall 2004, # 7 11 Gaussian Random Vectors Gaussian random variables: 1 n (x − µ )2o √ x fx(x) = exp − 2 for real x, σx 2π 2σx 2 1 n |x − µx| o fx(x) = 2 exp − 2 for complex x. σxπ σx Real Gaussian random vectors: 1 n o f (x) = exp − 1(x − µ )T R−1(x − µ ) . x N/2 1/2 2 x x x (2π) |Rx| Complex Gaussian random vectors: 1 n H −1 o fx(x) = N exp − (x − µx) Rx (x − µx) . π |Rx| Symbolic notation for real and complex Gaussian random vectors: x ∼ Nr(µx,Rx), real, x ∼ Nc(µx,Rx), complex. EE 524, Fall 2004, # 7 12 A linear transformation of Gaussian vector is also Gaussian, i.e. if y = Ax then T y ∼ Nr(Aµx, ARxA ) real, H y ∼ Nc(Aµx, ARxA ) complex. EE 524, Fall 2004, # 7 13 Complex Gaussian Distribution Consider joint pdf of real and imaginary part of a complex vector x x = u + jv. Assume z = [uT , vT ]T . The 2n-variate Gaussian pdf of the (real!) vector z is 1 1 T −1 fz(z) = exp − (z − µ ) R (z − µ ) , p 2n 2 z z z (2π) |Rz| where µu Ruu Ruv µz = ,Rz = . µv Rvu Rvv That is Z P [z ∈ Ω] = pZ(z) dz. z∈Ω EE 524, Fall 2004, # 7 14 Complex Gaussian Distribution (cont.) Suppose Rz happens to have a special structure: Ruu = Rvv and Ruv = −Rvu. T (Note that Ruv = Rvu by construction.) Then, we can define a complex Gaussian pdf 1 H −1 fx(x) = n exp −(x − µx) Rx (x − µx) , π |Rx| where µx = µu + jµv H Rx = E {(x − µx)(x − µx) } = 2(Ruu + jRvu) T 0 = E {(x − µx)(x − µx) }. EE 524, Fall 2004, # 7 15 Covariance of White Noise and Prewhitening Operation Covariance matrix for white uniform zero-mean noise: 2 Rx = σ I, where σ2 is the noise variance. Very important operation is prewhitening of a nonwhite process. 2 Assume that the process has the covariance matrix Rx 6= σ I. Then, prewhitening operation can be written as −1/2 y = WRx x where W is any unitary matrix (W H = W −1). H Ry = E {yy } −1/2 H −H/2 H = E {WRx xx Rx W } −1/2 H −H/2 H = WRx E {xx }E Rx W −1/2 −1/2 H = W Rx RxRx W = WW H = I. EE 524, Fall 2004, # 7 16 We can define an arbitrary (noninteger) power of Rx as N q X q H Rx = λi uiui . i=1 Prewhitening matrix is not unique! EE 524, Fall 2004, # 7 17 Discrete-time Stochastic Processes Definition. [Wide-Sense Stationarity (WSS)] A random process x(n) is WSS if its mean is a constant: E[x(n)] = µx, its autocorrelation rx(n1, n2) depends only on the difference n1 − n2: ∗ rx(n, n − l) = E [x(n)x (n − l)] = rx(l), and its variance is finite: 2 cx(0) = E {|x(n) − µx| } < ∞. Power Spectral Density (PSD): The PSD (or auto-PSD) of a stationary stochastic process is a Fourier transform of its autocorrelation sequence: ∞ jω X −jωl Px(e ) = rx(l)e . l=−∞ EE 524, Fall 2004, # 7 18 Linear Systems with Stationary Random Inputs: x(n) y(n) - Linear system T [·] - Output mean value: ∞ h X i µy = E [y(n)] = E h(k)x(n − k) k=−∞ ∞ ∞ X X j0 = h(k)E x(n − k) = µx h(k) = µxH(e ).
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