Algorithms for Adaptive Equalization MCQ’s

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This set of Wireless & Mobile Communications Multiple Choice Questions & Answers (MCQs) focuses on “Algorithms for Adaptive Equalization”.

1. Rate of convergence is defined by __________ of algorithm.
a) Time span
b) Number of iterations
c) Accuracy
d) Complexity

2. Computational complexity is a measure of ________
a) Time
b) Number of iterations
c) Number of operations
d) Accuracy

3. Which of the following factor could not determine the performance of algorithm?
a) Structural properties
b) Rate of convergence
c) Computational complexity
d) Numerical properties

4. Choice of equalizer structure and its algorithm is not dependent on ________
a) Cost of computing platform
b) Power budget
c) Radio propagation characteristics
d) Statistical distribution of transmitted power

5. Which of the following is not an algorithm for equalizer?
a) Zero forcing algorithm
b) Least mean square algorithm
c) Recursive least square algorithm
d) Mean square error algorithm

6. Coherence time is dependent on the choice of the algorithm and corresponding rate of convergence.
a) True
b) False

7. Which of the following is a drawback of zero forcing algorithm?
a) Long training sequence
b) Amplification of noise
c) Not suitable for static channels
d) Non zero ISI

8. LMS equalizer minimizes __________
a) Computational complexity
b) Cost
c) Mean square error
d) Power density of output signal

9. Which of the following algorithm uses simple programming?
a) LMS Gradient DFE
b) FTF algorithm
c) Fast Kalman DFE
d) Gradient Lattice DFE

10. Zero forcing algorithm performs well for wireless links.
a) True
b) False

11. For N symbol inputs, LMS algorithm requires ______ operations per iterations.
a) 2N
b) N+1
c) 2N+1
d) N2

12. Convergence rate of LMS is fast.
a) True
b) False

13. Stochastic gradient algorithm is also called ________
a) Zero forcing algorithm
b) Least mean square algorithm
c) Recursive least square algorithm
d) Mean square error algorithm

14. Which of the following does not hold true for RLS algorithms?
a) Complex
b) Adaptive signal processing
c) Slow convergence rate
d) Powerful

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