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Communication Theory Unit 4 Question Bank

Question Bank
Unit 4

1. What is FM threshold effect?(AU: MAY 2011)
As the carrier to noise ratio is reduced, clicks are heard in the receiver output. As the carrier to noise ratio reduces further, crackling, or sputtering sound appears at the receiver output. Near the breaking point, the theoretically calculated output signal to noise ratio becomes large, but its actual value is very small. This phenomenon is called threshold effect.
2. What is capture effect in FM? (AU: MAY12, DEC 10)
When the noise interference is stronger than FM signal, then FM receiver locks to
Interference. This suppresses FM signal. When the noise interference as well as FM signal are of equal strength, then the FM receiver locking fluctuates between them. This phenomenon is called capture effect.
3. What is meant by figure of merit of a receiver?(AU:DEC10)
The ratio of output signals to noise signal to channel signal to noise ratio is called figure of merit. It is the measurement of signal power in the channel. It is used much in communication system for identifying noise performance.
4. What is the Purpose of re-emphasis and de-emphasis in FM? (AU: DEC13, DEC10)
The PSD of noise at the output of FM receiver sally increases rapidly at high frequencies but the PSD of message signal falls off at higher frequencies. This means the message signal doesn’t utilize the frequency band in efficient manner. Such more efficient use of frequency band and improved noise performance can be obtained with the help of re-emphasis and de-emphasis.
5. What are extended threshold demodulators?
Threshold extension s also called threshold reduction. It is achieved with the help of FMFB demodulator. In the local oscillator is replaced by voltage controlled oscillator (VCO).The VC frequency changes as per low frequency variations of demodulated signal. Thus the receiver responds only to narrow band of noise centered around instantaneous carrier frequency. This reduces the threshold of FMFB receiver.
6. What is threshold effect with respect to noise?
When the carrier to noise ratio reduces below certain value, the message information is lost. The performance of the envelope detector deteriorates rapidly and it has no proportion with carrier to noise ratio. This is called threshold effect.
7. Define pre-emphasis and de-emphasis.
Pre-emphasis: It artificially emphasizes the high frequency components before modulation. This equalizes the low frequency and high frequency portions of the PSD and complete band is occupied.
De-emphasis: This circuit attenuates the high frequency components. The attenuation characteristic is exactly opposite to that of pre-emphasis circuit. De-emphasis restores the power distribution of the original signal. The signal to noise ratio is improved because of pre-emphasis and de-emphasis circuits.
8. State the reasons for higher noise in mixers.
1. Conversion trans-conductance of mixers is much lower than the
Trans-conductance of amplifiers
2. If image frequency rejection is inadequate, the noise associated with the image frequency also gets accepted.

9. Define signal to noise ratio.
Signal to noise ratio is the ratio of signal power to the noise power at the same point in a system. Signal-to-noise ratio (often abbreviated SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. It is defined as the ratio of signal power to the noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise
10. Define noise figure.
Noise figure is decibel representation of Noise factor. i.e., Noise figure = (Noise factor) dB
Noise figure (NF) and noise factor (F) are measures of degradation of the signal-to-noise ratio (SNR), caused by components in a radio frequency (RF) signal chain. It is a number by which the performance of an amplifier or a radio receiver can be specified, with lower values indicating better performance.

Part – B

1. Explain various sources and types of noise in detail                (AU: MAY 13, 14) (16)
2. a) Thermal noise from a resistor is 4x10^-17 Watts for given bandwidth and at temperature 20 degree Celsius. What is the noise power when T=50
And T=70°K                                                                                                    (AU: DEC 11) (8)
    b) Two resistors 20K and 50K at room temperature. Calculate for a bandwidth of 100 KHz, thermal noise for i)each resistor ii) in series iii) in parallel.              (AU: DEC 11) (2 marks)
    c) Define noise factor and noise figure                                   (AU: MAY 11, DEC 12) (2 marks)
    d) Define Friis’s Formula                                                      (AU: DEC 8) (2 Marks)
    e) Define noise temperature                                       (AU: MAY 13) (2 marks)
3. a) An amplifier1 has noise figure of 9DB and power gain of 15DB. It is connected in cascaded to another amplifier2 with noise figure 20DB. Calculate overall Noise figure.(AU: DEC10) (8)
   b) Consider two amplifiers in cascade. The first stage has power gain of 40DB and noise figure of 2DB, the second stage has noise figure of 3DB. Calculate overall noise figure.
(AU: MAY 11) (4)
   c) A receiver has a noise figure of 12 DB; it is fed by low noise amplifier with gain 50 DB and a noise temperature of 90K. Calculate the noise temperature of receiver and overall noise temperature at room temperature 290K                                                                (4 marks)
4. a) Define white noise and calculate power spectral density and autocorrelation of the white noise.                                                                                                       (AU: MAY 12) (4 marks)
    b) Explain in detail about Narrowband noise with envelope and phase components. (AU: MAY 11) (12 marks)
5. a) Describe in detail the noise performance of the AM receiver          (AU:MAY 13) (8 marks)
    b) Explain the noise performance of FM receiver in detail.     (AU: MAY 13) (8 marks)








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Communication Theory Unit 3 Quetion Bank

Unit 3 Question bank
1.      Define a random variable. Specify the sample space and the random variable for a coin tossing experiment                                                                             (Dec  2012)
A function which takes on any value from the sample space and its range is some set of real numbers is called a random variable of an experiment. For example, in a coin tossing experiment
Sample space = { H, T} H- Head, T- Tail
Random variable = {1,-1}i.e H=1 and tail=-1
2.      When a random process is called deterministic?                                        (Dec 2011)
When future values of any sample function can be predicted from knowledge of past values then the random process is called deterministic. For example: In a die throwing random experiment, if the future value of the throw is predicted from the past values then it is called deterministic.
3.      When the random process is said to be strict sense or strictly stationary? (May 2011)
The strictly stationary random process possesses following characteristics:
The statistical properties do not change with shift of time origin
A truly stationary process should start at minus infinity and end at infinity
4.      Write the Rayleigh and Rician probability density functions.                  (May 2011)
The Rayleigh distribution, named for William Strutt, Lord Rayleigh, is the distribution of the magnitude of a two-dimensional random vector whose coordinates are independent, identically distributed, mean 0 normal variables. The distribution has a number of applications in settings where magnitudes of normal variables are important
Rayleigh:
Rician:




5.      Differentiate between random variable and random process
S.No
Random Variable
Random Process
1
It is a set of numbers
It is a wave form
2
It need not be a function  of time
It is strictly function of time
3
These are not further classified
It can be stationary or ergodic
4
Only ensemble averages can be calculated
Ensemble as well as time averages can be calculated

6.      Define cumulative distribution function
The CDF of a random variable X is the probability that a random variable X takes a value less than or equal to x.
The variable F is cumulative distribution and P is probability.

7.      Give the properties of CDF
1.Every cumulative distribution function F is non-decreasing and right-continuous, which makes it a cad lag function.


2. if X is a purely discrete random variable, then it attains values x1, x2, ... with probability pi = P(xi), and the CDF of X will be discontinuous at the points xi and constant in between:


3. If the CDF F of a real valued random variable X is continuous, then X is a continuous random variable; if furthermore F is absolutely continuous, then there exists a Lebesgue-integrable function f(x) such that
8.      Define probability density function
The derivative of CDF with respect to some dummy variable is called PDF
The small f refers to density function and capital F refers to distribution function.
9.      Give the properties of PDF
The properties of probability distribution functions are
F(b) is cumulative distribution function
f(x) is probability density function with a dummy variable x
f(t) is probability density function with respect to time
10.  Define mean, auto correlation and cross correlation
The mean of random process is denoted by Mx(t).  The mean value is basically expected value of random process X(t). Autocorrelation is the similarity between observations as a function of the time lag between them. It is a mathematical tool for finding repeating patterns. Cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product.
16 marks
1.      (i) Explain continuous and discrete random variables with properties (4)
(ii) Explain different types of random process in detail (4)          Dec 2010, May 2012
      (iii) State and prove any 4 properties of auto correlation function (4)
      (iv)  Prove any 4 properties of cross correlation function (4)
2.      (i) A three digit message is transmitted over a noisy channel having a probability of error as P(E)=2/5 per digit. Find out the corresponding CDF (3)
(ii)Two dice are thrown at random several times. The random variable X assigns the sum of numbers appearing on dice to each outcome. Find the CDF for the random variable. (4)
 (iii) Two dice are thrown at random several times. The random variable X assigns the sum of numbers appearing on dice to each outcome. Find the PDF for the random variable. (5)
      (iv) Let X have the uniform distribution given by fx(X) = {1/2π, 0≤x≤2π
                                                                                                   {0, elsewhere
Calculate mean, variance and standard deviation (4) (May 14)
3.            (i) Given a random process X(t) = A cos(wt+θ), where A and w are constants and θ is a uniform random variable, show  that X(t) is ergodic in both mean and autocorrelation. (6)                                                                         May 2010
     (ii) Define central limit theorem with properties (6)                            May 2011
     (iii) Define Gaussian process and explain its properties in detail (4) May 2011

4.            (i) In a random process, bring out the five main properties of power spectral density (4)                                                                                   May2011
     (ii) Explain transmission of random process through LTI system (4)
     (iii) Define Ergodic process with ensemble averages and time averages and explain ergodicity in mean, variance and autocorrelation (4)
    (iv) Evolve Ergodic process is always stationary but converse is not true (4)
5.      A) A PDF of a random variable X is given by
fX (x) = { k(1-x2), 0≤x≤1
             {0, otherwise then find i) k ii)CDF iii) P(0≤X≤2)                             8marks
                  B) A random variable X has PDF:
fX (x) = {6x-6x2, 0≤x≤1
            { 0, otherwise.
Find a and b such  that P(X≤b) = P(X≥b) where b is between 0 and 1         8 marks

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