OPTICAL COMMUNICATION NETWORKS 2 MARK QUESTIONS

UNIT: I OPTICAL NETWORKING COMPONENTS

Part A (2 Marks for each question)

                                       

1. How do you measure sheath miles?
2. What do you understand by the term optical time division multiplexing?
3. What is a lightpath?
4. List the optical networking components.
5. Give the applications of directional couplers.
6. Differentiate between reciprocal and non-reciprocal devices.
7. What is a directional coupler?
8. State excess loss.
9. Differentiate between isolators and circulators.
10. What is insertion loss?
11. Write down any four requirements of a good optical filter.
12. Draw the structure of Fabry-Perot filter.
13. Mention the various methods of tuning Fabry-Perot filter.
14. Draw the structure of three cavity resonant dielectric thin film filter.
15. State the advantages of dielectric thin film filter.
16. Mention the applications of Mach-Zehnder Interferometer.
17. What is an arrayed waveguide?
18. Give the principle of operation of Acousto Optic Tunable filter.
19. State stimulated emission.
20. What is spontaneous emission?
21. State extinction ratio.
22. Differentiate between blocking and non-blocking switches.

Part A (2 Marks for each question)

                                       

1. What is SONET/SDH?
2. What is STS?
3. What does a SONET regenerator do?
4. What are the layers in a SONET standard?
5. What is the content of a byte in a SONET frame?
6. How do clocks function in SONET?
7. How is synchronous TDM multiplexing achieved in SONET?
8. Give the taxonomy of SONET networks.
9. Give the structure of an STS-N frame.
10. What is the function of an ADM?
11. How are devices connected in a SONET?
12. What is an ‘order wire byte’?
13. What is the contents of an SPE?
14. What is the user data rate of an STS-1 frame without considering the overhead?
15. What is byte interleaving?
16. Pictorially represent a point-to-point SONET network.
17. What are the configurations of a SONET ring network?
18. Find the data rate of an STS-3 signal.

UNIT: III BROADCAST AND SELECT NETWORKS

Part A (2 Marks for each question)

1. Mention the number of couplers used for an ‘n’ node network of star

and bus topologies.

2. Name the most popular topologies used for broadcast and select networks.
3. Define MAC protocol.
4. What is the output optical power of a 2x2 directional coupler?
5. What is the optimum value of α that minimizes Lbus?
6. What is a selective repeater?
7. How is performance measured in a MAC protocol?
8. What is the function of a synchronizer node?
9. On what assumption is the SA/SA family of protocols designed?
10. How does the basic SA/SA protocol operate?
11. What is wait-and-see-modification?
12. Define throughput per data channel.
13. How are the data and control slots different in DT-WDMA compared to SA/SA?
14. Define a frame with respect to DT-WDMA.
15. Define the (α, S) constraint of input traffic.
16. Name the three different traffic classes of a network.
17. Give the connection setup protocol used in Rainbow-1.
18. How is data sent in STARNET-II?
19. What is the wavelength partitioner used in lightning testbed.
20. Give the topology of the BBC television studio testbed.

UNIT: IV WAVELENGTH ROUTING NETWORKS

Part A (2 Marks for each question)

1.  Give the block diagram of a wavelength cross connect node.

2.   Name the types of regeneration techniques for digital data.

3.   Give the diagrammatic representation of the NTT ring testbed.

4. Differentiate between a static and a reconfigurable network.
5. Give the diagrammatic representation of the network design problem.
6. What is the use of wavelength conversion?
7. What the types of wavelength conversion that may be realized in a node?
8. What is the use of multiple fiber-pairs between nodes?
9. What is the degree of transparency of a fully optical network?
10. How are different types of WXC nodes realized?
11. Why is reliability needed in an optical switch?
12. How many wavelengths are required to build a reasonable network?
13.  How much do wavelength converters help in improving the capacity of a network?
14. What are good techniques for routing and assigning wavelengths to lightpaths?
15.  List the traffic models commonly employed to study optical networks.
16.  Give the bipartite graph representation of a static network.
17.  List the constraints of wavelength assignment for a network with undirected    

 lightpaths and edges.

18.  What is graph coloring?
19.  List the factors governing wavelength reuse.
20.  Draw the ONTC network testbed.

UNIT: V HIGH CAPACITY NETWORKS

Part A (2 Marks for each question)

1. List the approaches in increasing the transmission capacity on a link.
2. What are the drawbacks of the SDM approach?
3. Name the major system impairments the TDM faces.
4. Differentiate Unidirectional and Bidirectional WDM systems.
5. What is a soliton pulse? Where is it usually used?
6. Draw the optical Demux to extract one of the multiplexed channels from a packet-interleaved TDM stream.
7. Draw the block diagram of a soliton-trapping logical AND gate.
8. Define Synchronization with regard to a photonic packet-switching network.
9. What is an optical phase lock loop?
10. What are the functions of a routing node?
11. Draw the block diagram of a generic store and forward network.
12. Explain delay with respect to, i) deflection routing and ii) store and forward networks.
13. What is deflection index?
14. What is hot-potato routing?
15. Define Livelock.
16. Give an example of a 2x2 routing node using a  feed-forward delay line architecture.
17. What is the use of low differential loss characteristic in a network?
18. List some of the key features of OTDM testbeds.
19. Name some of the OTDM testbeds.
20. Draw the helical LAN topology proposed to be used in the AON TDM testbed.

ADVANCED DIGITAL SIGNAL PROCESSING 16 MARK QUESTIONS

   

16 MARKS:

UNIT I: DISCRETE RANDOM SIGNAL PROCESSING

(16 marks)

1. i)Derive the power spectral density of the random process.                (10)

ii) Determine the power spectrum of a WSS process having autocorrelation sequence rv(k)=(1/2)(A2coskω).                                    (6)

2. Explain Spectral factorization. What is regular process? State the properties of regular process.                                             (16)
3. Explain filtering random processes.                            (16)
4. i) Obtain the filter to generate a random process with a power spectrum of Px(ejω)= from white noise.                            (8)

ii) Explain Wide Sense Stationary process and its properties.                (8)

5. i) Explain Autocorrelation and Autocovariance matrices. Also explain its properties.(12)

ii) State and prove Wiener-Khintchine relation.                        (4)

6. i) Calculate the mean and variance of the autocorrelation process. Also explain the cross correlation.                                            (4)

ii) Find the autocorrelation sequence corresponding to the following power spectral densities

    a) Px(Z)=   

    b) Px(ejω)=                                (12)

7. i) Explain in detail about parameter estimation: Bias and Consistency.        (6)

ii) Find the output of a first order LTI system, when it is excited by a white noise with and if the variance of the white noise is equal to 1.        (10)

8. (i) State and prove Parseval’s theorem.                             (6)

(ii) A discrete sequence is given by x(n)={0.3,0.1,0.15,0.23,0.29,0.28}.

Compute a) Mean, b) Variance, c) Autocorrelation.                    (10)

9. i) Compute the autocorrelation for the given signal x(n)=(0.8)nu(n).            (8)

ii) Obtain the mean and autocorrelation of the real valued harmonic process         where A and are constants and φ is a random variable uniformly distributed over the interval –π to π.                            (8)

10. i) The power spectral density of a wide sense stationary process x(n) is Px(ejω)=.Find the whitening filter H(Z) that produces unit variance white noise when the input is x(n).                                    (10)

ii) Compute the PSD of the given signal x(n)={1,3}.                     (6)

        UNIT II: SPECTRUM ESTIMATION   

(16 marks)

1. Explain how the Periodogram is used to estimate the power spectrum?    (16)
2. Describe the performance measures of Periodogram based spectrum estimation technique.                                        (16)
3. The bias and variance of the Modified Periodogram is better than that of the ordinary Periodogram- Justify this statement analytically.            (16)
4. i) Explain power spectrum estimation using Barlett’s method.            (8)

ii) In the Welch method, calculate the variance of Welch power spectrum estimate with the Bartlett window if there is 50% overlap between successive sequences.                                        (8)

5. Derive the equation for power spectral density using Blackman-Tukey

method.                                        (16)

6. Compare the performance of periodogram, Bartlett, Welch and Blackman-Tukey methods of spectrum estimation.                            (16)
7. Derive the expression for spectral estimation based on Auto Regressive

model.                                         (16)

8. Discuss MA and ARMA model based power spectrum estimations.        (16)
9. Elucidate the Levinson Durbin algorithm of solving the normal equations.    (16)
10. Explain how the power spectral estimate is calculated using parametric method of AR model.                                        (16)
11. Explain the power spectrum estimation using model based techniques.    (16)

UNIT III: LINEAR ESTIMATION AND PREDICTION   

(16 marks)

1. Explain the steps in the design of FIR wiener filter that produces the minimum mean square estimate of the process.                            (16)
2. Briefly explain forward linear prediction and backward linear prediction.        (16)
3. Derive the expression of Discrete Kalman filter.                    (16)
4. Design a three step predictor for a random process having an autocorrelation sequence of the form rx(k)= δ(k)+(0.9) ׀k׀cos(πk/4). Determine the estimate of x(n+3) and also MMSE for the same. Compare the MMSE of multistep predictor with single step predictor.                                             (16)
5. Explain in detail about Causal and Noncausal IIR filter.                (16)
6. Discuss in detail how Levinson’s Durbin algorithm is used to solve the normal equations.                                        (16)
7. An AR(2) process has the autocorrelation sequence of rd(k)=αIkI with 0<α<1. Suppose d(n) is observed in the presence of uncorrelated white noise v(n), that has a variance of σv2.δ(k) and x(n)=d(n)+v(n). Design a second order FIR wiener filter to reduce the noise in x(n) with w(z)=w(0)+w(1)Z-1.Assume α=0.8.              (16)
8. If the values of rx(k) for lags k-0 to 4 are rx(k)=[1.0, 0, 0.1, -0.2, -0.9]T .Solve the Wiener Holf equations and find the optimum three step predictor.        (16)
9. Describe how the kalman filter is used to estimate an unknown constant.    (16)
10. An AR(1) process has the autocorrelation sequence of rd(k)=αIkI with 0<α<1. Suppose d(n) is observed in the presence of uncorrelated white noise v(n), that has a variance of σv2.δ(k) and x(n)=d(n)+v(n). Design a first order FIR wiener filter to reduce the noise in x(n) with w(z)=w(0)+w(1)Z-1.Assume α=0.8.            (16)

          UNIT IV: ADAPTIVE FILTERS   

(16 marks)

1. Explain steepest decent adaptive filter.                        (16)
2. Describe LMS algorithm for determining FIR adaptive filter coefficients and obtain its condition for convergence.                                (16)
3. Briefly explain Adaptive Channel Equalization and Adaptive Echo Cancellation.(16)
4. Explain in detail about exponentially weighted RLS.                (16)
5. Give complete discussion on how LMS algorithm is converging with necessary derivation of equation.                                (16)
6. Derive the design equations for FIR adaptive wiener filter that would minimize the exponentially weighted least mean square error.                (16)
7. i) Describe sliding window RLS and derive its update equation.        (8)

ii) Explain how an Adaptive filter can be used as a noise canceller with a block diagram.                                         (8)

8. Write a detailed note on any two applications of adaptive filters with neat

diagram.                                         (16)

9. i) State the difficulty in the design and implementation of LMS adaptive filter and describe how this problem is overcome with normalized LMS algorithm.    (8)

ii) Derive the weight vector update equation for the LMS algorithm.        (8)

10. Explain adaptive linear prediction using LMS algorithm.            (16)

UNIT V: MULTIRATE DIGITAL SIGNAL PROCESSING

    (16 marks)

1. Explain the mathematical description of change of sampling rate.        (16)
2. Define decimation and interpolation. Derive the equation of decimation and interpolation factor.                                    (16)
3. Discuss sampling rate conversion by a rational factor I/D.            (16)
4. Explain in detail the filter bank implementation of wavelet transforms.    (16)
5. Discuss how signal compression can be achieved using sub-band coding?    (16)
6. Discuss sampling rate conversion with time variant structures.        (16)
7. Briefly explain the polyphase structures for decimation and interpolation filters.(16)    
8. Describe the multistage implementation of multirate system.            (16)
9. Define wavelet transform. Describe filter bank implementation of wavelet expansion of signals. Discuss one application of wavelet transform.        (16)
10. Explain the application of multirate processing in sub band coding.        (16)
11. Describe the implementation of sampling rate conversion using direct form FIR structures.                                        (16)