通信原理课件第01章(对映haykin第四版共10章).pptVIP

 Chapter 1Random Processes Figure 1.1An ensemble of sample functions. Figure 1.2Illustrating the probability of a joint event. Figure 1.3Illustrating the concept of stationarity in Example 1.1. Figure 1.4Illustrating the autocorrelation functions of slowly and rapidly fluctuating random processes. Figure 1.5Autocorrelation function of a sine wave with random phase. Figure 1.6Sample function of random binary wave. Figure 1.7Autocorrelation function of random binary wave. Figure 1.8Transmission of a random process through a linear time-invariant filter. Figure 1.9Magnitude response of ideal narrowband filter. Figure 1.10Power spectral density of sine wave with random phase; ?(f) denotes the delta function at f = 0. Figure 1.11Power spectral density of random binary wave. Figure 1.12A pair of separate linear time-invariant filters. Figure 1.13Normalized Gaussian distribution. Figure 1.14Sample function of a Poisson counting process. Figure 1.15Models of a noisy resistor. (a) Thévenin equivalent circuit. (b) Norton equivalent circuit. Figure 1.16Characteristics of white noise. (a) Power spectral density. (b) Autocorrelation function. Figure 1.17Characteristics of low-pass filtered white noise. (a) Power spectral density. (b) Autocorrelation function. Figure 1.18(a) Power spectral density of narrowband noise. (b) Sample function of narrowband noise. Figure 1.19(a) Extraction of in-phase and quadrature components of a narrowband process. (b) Generation of a narrowband process from its in-phase and quadrature components. Figure 1.20Characteristics of ideal band-pass filtered white noise. (a) Power spectral density. (b) Autocorrelation function. (c) Power spectral density of in-phase and quadrature components. Figure 1.21Illustrating the coordinate system for representation of narrowband noise: (a) in terms of in-phase and quadrature components, and (b) in terms of envelope and phase. Figure 1.22Normalized Rayleigh distribution. Figure 1.23Normaliz