Lecture 2 Review of Signals and Systems: Part 1. EE4900/EE6720 Digital Communications
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1 EE4900/EE6420: Digital Communications 1 Lecture 2 Review of Signals and Systems: Part 1
2 Block Diagrams of Communication System Digital Communication System 2 Informatio n (sound, video, text, data, ) Transducer & A/D Converter Source Encoder Channel Encoder Modulator Tx RF System Channel Output Signal D/A Converter and/or output transducer Source Decoder Channel Decoder Demodulator Rx RF System = Discrete-Time = Continuous-Time
3 Why Signals and Systems? Wireless/Wired Communications is fundamentally a transmission-of-information problem Information is transmitted through channel/medium Information is transmitted via signals through systems Real-world (outside of the embedded hardware) consists of phenomenon of continuous-time, t After sampling, the embedded hardware can be thought to run on discrete-time, nt 3 Informatio n (sound, video, text, data, ) x(t) x(nt)=x(n) x(t) Transducer & A/D Converter Source Encoder Channel Encoder Modulator Tx RF System
4 Continuous-time Signals Energy and Power of periodic signal, x(t) Energy: eq. 2.2 Power: eq Autocorrelation function, r x (t) for energy signal, eq. 2.4 Autocorrelation function, ϕ x (t) for power signal, eq. 2.5 Fourier-series representation of the signal x(t), eq
5 Continuous-time Signals Fourier-series representation of the signal, x(t) 5 A periodic signal can be represented by its Fourier series as an infinite sum of sines and cosines
6 Continuous-time Signals The Impulse Function, δ(t) The Impulse function is zero everywhere except at t=0: eq The sifting property (sampling): 6 t 2 x τ, න x t δ t τ dt = ቊ t 0, t 1 1 τ t 2 elsewhere
7 Continuous-time Signals The Unit-step Function, u(t) The Unit-step function is 1 everywhere except t<=0 The Unit-step function represents switching (on/off): t 2 t < 0 u t = න δ τ dτ = ቐ0, undefined, t = 0 1, t > 0 7
8 Discrete-time Signals When a continuous-time signal x(t) is sampled, it becomes a discrete-time signal x(n) 8 Energy and Power of periodic signal, x(n) Energy: eq Power: eq Fourier-series representation of the signal x(n), eq Discrete-time impulse function δ(n) and step function u(n) δ(n) = ቊ 1, 0, n = 0 n 0 u(n) = ቊ 1, 0, n 0 n < 0
9 Continuous-time Systems Continuous-time system is a collection of continuoustime components such as resistors, capacitors, inductors, transistors Linear, Time-Invariant (LTI) System produces output y(t) with same delay as the input x(t) LTI System is characterized by its impulse response h(t) δ(t) Impulse aka shock LTI System h(t) Response to the shock 9 Convolution Integral: find the output y(t) given the input x(t) for the system with impulse response h(t)
10 Continuous-time Systems Convolution: eq Application of Convolution: Filter When two signals are convolved in time-domain, they can be multiplied in frequency-domain: filter Time-Domain x(t) Input Signal h(t) Filter y(t)=x(t)*h(t) Output Signal 10 Frequency- Domain X(ω) H(ω) Y(ω)=X(ω)H(ω) 0 Hz 1 khz 5 khz 20 khz 30 khz 0 Hz 1 khz 5 khz 20 khz 30 khz
11 Continuous-time Systems Laplace Transform, eq Complex Frequency, s=σ+jω Purpose: convert time-domain signal into frequency-domain (s-domain) signal
12 Continuous-time Systems Pole-zero plot: poles lift the amplitude in s-domain, zeros pin the amplitude in s-domain 12
13 Continuous-time Systems Fourier Transform: a special case of Laplace Transform, with s=σ+jω; where σ=0. Recall that this indicates sustained oscillations (critically-damped case) 13 Laplace Transform Fourier Transform
14 Continuous-time Systems Fourier Transform, eq
15 Energy of Baseband signal vs. Bandpass signal 15
16 Discrete-time Systems Examples of Discrete-time Systems: embedded computing with memory and microprocessor, digital logic circuits Convolution Sum: find the output samples y(n) given the input samples x(n) for the system with impulse response h(n) 16 Time-Domain x(n) h(n) y(n)=x(n)*h(n) Input Signal Filter Output Signal Frequency- Domain X(Ω) H(Ω) Y(Ω)=X(Ω)H(Ω)
17 Linear Constant-coefficient Difference Equation Input and output relationship of discrete-time system Implemented by multipliers, adders, delay (memory) blocks Suppose that 1) Input signal samples are denoted by x(n) and output by y(n) 2) The filter coefficients are denoted by numerator b 0,, b M and denominator a 0,, a M with filter order=m 17 Equation 2.24
18 Discrete-time Systems Z-Transform, eq Complex Frequency, z=re{z}+jim{z} Purpose: convert discrete time-domain signal into discrete frequency-domain (z-domain) signal 18
19 Discrete-time Systems Pole-zero plot: poles lift the amplitude in z-domain, zeros pin the amplitude in z-domain Discrete Fourier Transform: a special case of Z-Transform, with z=e jω 19
20 Discrete-time Systems Discrete-time Fourier Transform (DTFT), eq
21 Discrete-time Systems Periodicity of DTFT, X(e jω ) is periodic at every 2π 21 1 period of the discrete-time signal 3 periods of the discrete-time signal 4 periods of the discrete-time signal
22 Discrete-time Systems Ω is a continuous variable! To represent the DTFT in digital logic hardware, Ω must be sampled (discrete values) When the DTFT X(e jω ) is sampled at interval 2π/N, it is called Discrete Fourier Transform (DFT): eq
23 Sampling Theorem: Section Application: A/D 23
24 Application: D/A Ideal Low Pass Filtering 24 Discrete-time samples LPF Continuous-signal
25 Discrete-time Processing 25 Slide 23 Slide 24
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