An in-depth examination regarding the principles of communication theory as applied to the transmission of information. Preserving its successful introduction of Fourier analysis, this edition has almost doubled the material on digital communication. Includes a new appendix on cryptography and uses computer experiments to illustrate important concepts.
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Representation of Signals and Systems
Random Signals and Noise
Amplitude Modulation Systems
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amplitude modulation amplitude spectrum applied approximation assume autocorrelation function band band-pass filter baseband binary carrier wave carrier-to-noise ratio channel complex envelope Consider constant correlation corresponding cos(2nfct cross-correlation defined delta function demodulator denote Determine distortion DSBSC wave duration envelope detector equal equivalent evaluate Example Figure filter output FM wave follows frequency modulation gp(t Hilbert transform illustrated in Fig in-phase input signal integral interval linear low-pass filter matched filter mean power message signal mid-band frequency modulation systems noise power obtained oscillator output signal output signal-to-noise ratio phase phase-locked loop probability density function Problem product modulator quadrature component quantizing random process X(t random variable receiver output rectangular pulse relation result sample function sequence shown in Fig shown plotted side-frequencies signal g(t signal-to-noise ratio sinusoidal wave spectral density spectral density N0/2 SSB wave symbol transfer function transmission bandwidth transmitted voltage wave s(t white Gaussian noise zero