Ece v Analog Communication [10ec53] Notes

Analog communication (10EC53)

1 CITSTUDENTS.IN

SYLLABUS ANALOG COMMUNICATION

Subject Code : 10EC53 IA Marks : 25 No. of Lecture Hrs/Week : 04

Exam Hours : 03 Total no. of Lecture Hrs. : 52 Exam Marks : 100

PART A

UNIT - 1 RANDOM PROCESS: Random variables: Several random variables. Statistical

averages: Function of Random variables, moments, Mean, Correlation and Covariance

function: Principles of autocorrelation function, cross correlation functions. Central

limit theorem, Properties of Gaussian process.

7 Hours

UNIT - 2

AMPLI TUDE MODULATI ON: Introduction, AM: Time-Domain description, Frequency Domain description. Generation of AM wave: square law modulator,

switching modulator. Detection of AM waves: square law detector, envelop detector.

Double side band suppressed carrier modulation (DSBSC): Time-Domain description,

Frequency-Domain representation, Generation of DSBSC waves: balanced modulator,

ring modulator. Coherent detection of DSBSC modulated waves. Costas loop. 7

Hours

UNIT - 3

SINGLE SIDE-BAND MODULATION (SSB): Quadrature carrier multiplexing, Hilbert transform, properties of Hilbert transform, Pre envelope, Canonical representation

of band pass signals, Single side-band modulation, Frequency-Domain description of

SSB wave, Time-Domain description. Phase discrimination method for generating an

SSB modulated wave, Time-Domain description. Phase discrimination method for

generating an SSB modulated wave. Demodulation of SSB waves. 6 Hours

UNIT - 4

VESTI GIAL SIDE-BAND MODULATION (VSB): Frequency Domain description, Generation of VSB modulated wave, Time - Domain description, Envelop detection of

VSB wave plus carrier, Comparison of amplitude modulation techniques, Frequency

translation, Frequency division multiplexing, Application: Radio broadcasting, AM radio.

6 Hours

UNIT - 5 PART B

ANGLE MODULATI ON (FM)-I: Basic definit ions, FM, narrow band FM,wide band

FM, transmission bandwidth of FM waves, generation of FM waves: indirect FM and

direct FM. 6 Hours

UNIT - 6

ANGLE MODULATI ON (FM)-II: Demodulation of FM waves, FM stereo multiplexing, Phase-locked loop, Nonlinear model of the phase locked loop, Linear

model of the phase locked loop, Nonlinear effects in FM systems.

7 Hours


2 CITSTUDENTS.IN

UNIT - 7 NOISE: Introduction, shot noise, thermal noise, white noise, Noise equivalent

bandwidth, Narrow bandwidth, Noise Figure, Equivalent noise temperature, cascade

connection of two-port networks. 6 Hours

UNIT - 8

NOISE IN CONTIN UOUS WAVE MODULATION SYSTEMS:

Introduction, Receiver model, Noise in DSB-SC receivers, Noise in SSB receivers, Noise in AM receivers, Threshold effect, Noise in FM receivers, FM threshold effect,

Pre-emphasis and De-emphasis in FM,.

7 Hours

TEXT BOOKS: 1. Communication Systems, Simon Haykins, 5th Edition, John Willey, India Pvt. Ltd,

2009.

2. An Introduction to Analog and Digital Communication, Simon Haykins, John Wiley

India Pvt. Ltd., 2008

REFERENCE BOOKS:

1. Modern digital and analog Communication systems B. P. Lathi, Oxford University Press., 4th ed, 2010,

2. Communication Systems, Harold P.E, Stern Samy and A Mahmond, Pearson Edn,

2004.

3. Communication Systems: Singh and Sapre: Analog and digital TMH 2nd , Ed 2007.


3 CITSTUDENTS.IN

INDEX SHEET

SL.NO TOPIC PAGE NO.

I UNIT 1 RANDOM PROCESS 4-8 II UNIT 2 AMPLITUDE MODULATION 9-24

III UNIT 3 SINGLE SIDE-BAND

MODULATION (SSB) 25-39

IV UNIT 4 VESTIGIAL SIDE-BAND

MODULATION 40-46

V UNIT 5 ANGLE MODULATION (FM)-I 47-56 VI UNIT 6 ANGLE MODULATION (FM)-II 57-62 VII UNIT 7 NOISE: 63-67

VIII

UNIT 8 NOISE IN CONTINUOUS

WAVE MODULATION SYSTEMS: 68-73


4 CITSTUDENTS.IN

UNIT 1

RANDOM PROCESS: Random variables: Several random variables. Statistical

averages: Function of Random variables, moments, Mean, Correlation and Covariance

function: Principles of autocorrelation function, cross correlation functions. Central

limit theorem, Properties of Gaussian

process. 7 Hrs

TEXT BOOK S: 1. Communication Systems, Simon Haykins, 5th Edition, John Willey, India Pvt. Ltd,

2009.

2. An Introduction to Analog and Digital Communication, Simon Haykins, John

Wiley India Pvt. Ltd., 2008

REFERENCE BOOKS:

1. Modern digital and analog Communication systems B. P. Lathi, Oxford University

Press., 4th ed, 2010

2. Communication Systems, Harold P.E, Stern Samy and A Mahmond, Pearson Edn,

2004.

3. Communication Systems: Singh and Sapre: Analog and digital TMH 2nd , Ed 2007.


5 CITSTUDENTS.IN

1.1. Random Process A random process is a signal that takes on values, which are determined (at least in part) by chance. A sinusoid with amplitude that is given by a random variable is an example of

a random process. A random process cannot be predicted precisely. However, a

deterministic signal is completely predictable.

An ergodic process is one in which time averages may be used to replace ensemble

averages. As signals are often functions of time in signal processing applications,

egodicity is a useful property. An example of an ensemble average is the mean win at the

blackjack tables across a whole casino, in one day. A similar time average could be the

mean win at a particular blackjack table over every day for a month, for example. If these

averages were approximately the same, then the process of blackjack winning would

appear to be ergodic. Ergodicity can be difficult to prove or demonstrate, hence it is often

simply assumed.

1.2. Probabilistic Descr iption of a Random Process

Although random processes are governed by chance, more typical values and trends in

the signal value can be described. A probability density function can be used to describe

the typical intensities of the random signal (over all time). An autocorrelation function

describes how similar the signal values are expected to be at two successive time

instances. It can distinguish between erratic ignals versus a lazy random wa

1.3. Autocor relation

If the autocorrelation for a process depends only on the time difference t2 t1, then the

random process is deeme wide sense stationary For a WSS process:

For a WSS, ergodic, process autocorrelation may be evaluated via a time average (Autocorrelation function, for continuous-time)

(Autocorrelation sequence, for discrete-time)

6 CITSTUDENTS.IN


Crosscorrelation Function for a Stationary Process

1.4. Power Spectral Density For a stationary process, the Wiener-Khinchine relation states:

Where denotes Fourier transform. can be thought of as the expected powe the

random process. This is because

Where is a truncated version of the random process. The quantity

is known as the periodogram of X(t). The PSD may be estimated computationally, by

taking the average of many examples of the power spectrum, over a relatively long

intervals of the process X(t). This reveals the expected power density at various

frequencies. (One such interval of X(t) would not typically have the power density of the

time averaged version).

Common Random Processes and Sequences Gauss-Markov Random Process (Continuous)

7 CITSTUDENTS.IN

1

Analog communication (I OEC53)

l 'ru loubil ity l l':'Ui ly t'uu.:li.m:(.;m.::.n.iun, wi.lh :,:.,.1"->H uml \'U mr -., (1::.

R,_.(r)-(,0!. ,-:t S, + ft ') Suniona.ey and. phyaicQJly rKi l y f'u udiun:;!;.-,,,, \....i1i1 l'.f!IU mt \'.111 (:11) So.:np:6$ of (dhc-tEot ,l tf "'\Jm have mar;nituoi>90't . Sp ctrum ie

bad-l i ni.l -td ..Ju., lu llp, !''$ !S"'m ,(., w ln tirmt'll l ..ril 11f:u l i r.., n. \J$:'"'$:1.

Sir..n ru -cbnnEs '"'int'i.t!.itelv lb.r , inJjnite;fnst"

i l,,,nci -1i n,itod Wh i tQ Pr."lt>.O$:$; ((!0"\ r.ri n ur,l t $:)

Pr.-..b,,hil if: \1 n...n$;r.y [T1J n ro.ri .-..n:lln$:f' l"'r.ifi .o::wi - r'n :.u;r. u:: - 2W A

R,.(T) = 2Jt ..jsi n(2f:nT) .'c'2;r lf".' ) S ,.(iw) is n ta(tMgu lnr puhe wi< h nop!iruC:e A u cl buu d ...ioHh '2 pi'A' (Wiu H-.)r Stationl\r.? and phys.i.;llty Nlizab:e.

Wr itc(!'r 1-'r!!I:;("1(;,l l! ll ctn WJoI l!T "H ro, 11i:t11'II:! I i nu 1((:o1! 1f i ru 1111;)

v...tnwd w; X(t) - J' n(11)du :ht.r:.:(1 t 1. (.O;... ,u ;o; ia u W Ulo. J.l:-u.o. c

Urm-.:truph;.

8 CITSTUDENTS.IN


Recommended questions:

1. Define mean, Auto correlation and auto co variance of a random process

2. Define power spectral density and explain its properties.

3. Explain cross correlation and Auto correlation.

4. State the properties of Gaussian process?

5. Define conditional probability, Ramdom variable amd mean?

6. Explain Joint probability of the events A & B?

7. Explain Conditional probability of the events A & B?

8. Explain the properties Gaussian process?

9. Compare auto correlation and cross correlation functions?

Documents

Ece v Analog Communication [10ec53] Notes