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  • 1

    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

    AMPLITUDE MODULATION: 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

    VESTIGIAL 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 MODULATION (FM)-I: Basic definitions, 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 MODULATION (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

    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 CONTINUOUS 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

    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

    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 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.

  • 5

    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 Description 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‟ signals versus a „lazy random walk‟.

    1.3. Autocorrelation

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

    random process is deemed „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

    Analog communication (10EC53)

    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 power‟ of 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

    Analog communication(IOEC53)

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  • 8

    Analog communication (10EC53)

    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