Chapter 6

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Communication Systems

Instructor: Engr. Dr. Sarmad Ullah Khan

Assistant ProfessorAssistant ProfessorElectrical Engineering Department

CECOS University of IT and Emerging [email protected]

Chapter 6

Dr. Sarmad Ullah Khan

Sampling and Analog to Digital Conversion

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Outlines

• Sampling Theorem

P l C d M d l ti (PCM)


• Pulse Code Modulation (PCM)

• Noise

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Outlines





• Noise

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Sampling Theorem

• A signal g(t) whose spectrum is band limited to BHz can be represented as


Hz can be represented as

G(f) = 0 if |f| > B

• Signal reconstruction requires that sampling rateshould beshould be

R > 2BSampling frequency = fs > 2B Hz

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Sampling Theorem


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Sampling Theorem

• Mathematically it can be represented as


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Sampling Theorem



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Sampling Theorem



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Sampling Theorem


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Sampling Theorem


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Sampling Theorem


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Sampling Theorem


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Outlines





• Noise

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Pulse Code Modulation


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Outlines





• Noise

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Noise

• In any real physical system, when the signal voltagearise at the demodulator it will be accompanied by


arise at the demodulator, it will be accompanied bya voltage waveform which varies with time in anentirely unpredictable manner. This unpredictablevoltage wave form is a random process called noise

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Noise

• Types of NoiseMost man made electro magnetic noise occurs at


Most man made electro-magnetic noise occurs at frequencies below 500 MHz. The most significant of these include:

• Hydro lines • Ignition systems • Fluorescent lightsFluorescent lights • Electric motors

Therefore deep space networks are placed out in the desert, far from these sources of interference.

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Noise

• There are also a wide range of natural noise sourceswhich cannot be so easily avoided, namely:


y , y• Atmospheric noise - lighting < 20 MHz• Solar noise - sun - 11 year sunspot cycle• Cosmic noise - 8 MHz to 1.5 GHz• Thermal or Johnson noise. Due to free electrons

striking vibrating ions.• White noise white noise has a constant spectral• White noise - white noise has a constant spectral

density over a specified range of frequencies.Johnson noise is an example of white noise.

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Noise

• Gaussian noise - Gaussian noise is completelyrandom in nature however, the probability of


, p yany particular amplitude value follows thenormal distribution curve. Johnson noise isGaussian in nature.

• Shot noise - bipolar transistors (caused byrandom variations in the arrival of electrons orholes at the output electrodes of an amplifyingdevice)device)

• Transit time noise - occurs when the electrontransit time across a junction is the same periodas the signal.

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Noise

• The noise power is given by:

P kTB


Pn = kTB

• Where:

• k = Boltzman's constant (1.38 x 10-23 J/K)

• T = temperature in degrees Kelvin

• B = bandwidth in Hz

• If the two signals are completely random with respect to each other, such as Johnson noise sources, the total power is the sum of all of the individual powers:

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Noise

• A Johnson noise of power P = kTB, can be thought ofas a noise voltage applied through a resistor


as a noise voltage applied through a resistor,Thevenin equivalent.

• An example of such a noise source may be a cable ortransmission line. The amount of noise powertransferred from the source to a load, such as anamplifier input is a function of the source and loadamplifier input, is a function of the source and loadimpedances

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Noise


• The rms noise voltage at maximum power transfer is

• Observe what happens if the noise resistance isresolved into two components

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Noise

• The terms used to quantify noise :

Si l t i ti I i i h i l


• Signal to noise ratio: It is either unit-less orspecified in dB. The S/N ratio may be specifiedanywhere within a system.

• Noise Factor (or Noise Ratio):

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Noise

• This parameter (i.e. Noise Figure ) is specified in allhigh performance amplifiers and is measure of how


high performance amplifiers and is measure of howmuch noise the amplifier itself contributes to the totalnoise. In a perfect amplifier or system, NF = 0 dB.This discussion does not take into account any noisereduction techniques such as filtering or dynamicemphasisp

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Noise

• Friiss' Formula & Amplifier CascadesIt i i t ti t i lifi d t h


– It is interesting to examine an amplifier cascade to see how noise builds up in a large communication system

– Amplifier gain can be defined asp g

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Noise

• Friiss' Formula & Amplifier CascadesA d th i f t ( ti ) b itt


– And the noise factor (ratio) can be rewritten as

– The output noise power can now be written

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Noise

• Friiss' Formula & Amplifier CascadesF thi b th t th i t i i i d b


– From this we observe that the input noise is increased bythe noise ratio and amplifier gain as it passes through theamplifier. A noiseless amplifier would have a noise ratio(factor) of 1 or noise figure of 0 dB. In this case, the inputnoise would only be amplified by the gain since theamplifier would not contribute noise

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Friiss' Formula

Chapter 6

Engineering

Transcript of Chapter 6