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White noise PSDF and AC; properties of autocorrelation

Short answer

White noise has flat PSD S(f) = N0/2 for all f, leading to autocorrelation function R(τ) = (N0/2)δ(τ). Key properties of autocorrelation include even symmetry, maximum at zero lag equal to average power, a Fourier pair with PSD via Wiener–Khinchin, periodicity for periodic signals, and convergence to the squared mean for large lags.

Define white noise with PSDF and auto correlation function. State the properties of auto correlation function.

White noise, its PSDF and AC function

White noise is an idealised random noise whose power spectral density is flat (constant) over all frequencies, like white light containing all colours. Thermal and shot noise are nearly white up to very high frequencies (∼1012\sim 10^{12} Hz), so the model is accurate for any practical receiver bandwidth. It is usually also assumed Gaussian, zero-mean (AWGN).

PSD (two-sided):

SW(f)=N02for all fS_W(f)=\frac{N_0}{2}\quad\text{for all } f

where N0=kTeN_0=kT_e (W/Hz), kk = Boltzmann constant and TeT_e = equivalent noise temperature.

Autocorrelation (inverse Fourier transform of PSD, using F−1{1}=δ(τ)\mathcal{F}^{-1}\{1\}=\delta(\tau)):

RW(τ)=∫−∞∞N02ej2πfτdf=N02 δ(τ)R_W(\tau)=\int_{-\infty}^{\infty}\frac{N_0}{2}e^{j2\pi f\tau}df=\frac{N_0}{2}\,\delta(\tau)
   S_W(f)                      R_W(tau)
     |                             ^  (N0/2) delta
 N0/2+-----------------            |
     |                             |
 ----+-----------------> f   ------+------> tau
     0                             0

Meaning: RW(τ)=0R_W(\tau)=0 for every τ≠0\tau\ne0, so any two samples of white noise, however close, are uncorrelated (and independent if Gaussian). The total power RW(0)=∫SW(f)dfR_W(0)=\int S_W(f)df is infinite, which is why white noise is only a model; after any filter with finite bandwidth the power becomes finite.

Properties of the autocorrelation function

  1. Even symmetry: RX(−τ)=RX(τ)R_X(-\tau)=R_X(\tau).
  2. Value at origin = mean-square value (power): RX(0)=E[X2(t)]=PR_X(0)=E[X^2(t)]=P (for an energy signal, Rg(0)=ER_g(0)=E).
  3. Maximum at origin: ∣RX(τ)∣≤RX(0)\lvert R_X(\tau)\rvert\le R_X(0) for all τ\tau.
  4. Fourier pair with PSD (Wiener–Khinchin): SX(f)=∫−∞∞RX(τ)e−j2πfτdτS_X(f)=\int_{-\infty}^{\infty}R_X(\tau)e^{-j2\pi f\tau}d\tau and RX(τ)=∫−∞∞SX(f)ej2πfτdfR_X(\tau)=\int_{-\infty}^{\infty}S_X(f)e^{j2\pi f\tau}df.
  5. Periodicity: if x(t)x(t) is periodic with period T0T_0, RX(τ)R_X(\tau) is also periodic with T0T_0.
  6. DC component: if X(t)X(t) has mean mm and no periodic part, RX(τ)→m2R_X(\tau)\to m^2 as τ→∞\tau\to\infty.

White noise satisfies these: N02δ(τ)\frac{N_0}{2}\delta(\tau) is even, maximum at τ=0\tau=0, and its Fourier transform is the flat PSD.

✓ Answer checked by Claude Opus 5.5 (high effort)

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