# Algorithms for Adaptive Equalization

This set of Wireless & Mobile Communications Questions and Answers for Campus interviews focuses on “Algorithms for Adaptive Equalization”.

1. 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

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

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

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

6. 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

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

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

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

11. 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

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

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

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