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

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Explanation: The performance of an algorithm is determined by various factors. These factors are rate of convergence, computational complexity and numerical properties. The performance of algorithm does not depend on structural properties.

2. Rate of convergence is defined by _____ of algorithm.

a) Time span

b) Number of iterations

c) Accuracy

d) Complexity

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Explanation: Rate of convergence is required as number of iterations required for the algorithm to converge close enough to the optimum solution. It enables the algorithm to track statistical variations when operating in non stationary environment.

3. Computational complexity is a measure of ________

a) Time

b) Number of iterations

c) Number of operations

d) Accuracy

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Explanation: Computational complexity is the number of operations required to make one complete iteration of the algorithm. It helps in comparing the performance with other algorithms.

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

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Explanation: The cost of the computing platform, the power budget and the radio propagation characteristics dominate the choice of an equalizer structure and its algorithm. Battery drain at the subscriber unit is also a paramount consideration.

5. Coherence time is dependent on the choice of the algorithm and corresponding rate of convergence.

a) True

b) False

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Explanation: The choice of algorithm and its corresponding rate of convergence depends on the channel data rate and coherence time. The speed of the mobile unit determines the channel fading rate and the Doppler spread, which is directly related to coherence time of the channel.

6. Which of the following is an algorithm for equalizer?

a) Zero forcing algorithm

b) Least mean square algorithm

c) Recursive least square algorithm

d) Mean square error algorithm

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Explanation: Three classic equalizer algorithm are zero forcing (ZF) algorithm, least mean squares (LMS) algorithm and recursive least squares (RLS) algorithm. They offer fundamental insight into algorithm design and operation.

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

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Explanation: The zero forcing algorithm has the disadvantage that the inverse filter may excessively amplify noise at frequencies where the folded channel spectrum has high attenuation.

8. Zero forcing algorithm performs well for wireless links. State whether True or False.

a) True

b) False

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Explanation: ZF is not often used in wireless links as it neglects the effect of noise altogether. However, it performs well for static channels with high SNR, such as local wired telephone links.

9. LMS equalizer minimizes __________

a) Computational complexity

b) Cost

c) Mean square error

d) Power density of output signal

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Explanation: LMS equalizer is a robust equalizer. It is used to minimize mean square error (MSE) between the desired equalizer output and the actual equalizer output.

10. For N symbol inputs, LMS algorithm requires ______ operations per iterations.

a) 2N

b) N+1

c) 2N+1

d) N2

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Explanation: The LMS algorithm is the simplest algorithm. For N symbol inputs, it requires only 2N+1 operations per iteration.

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

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Explanation: The minimization of the MSE is carried out recursively, and it can be performed by the use of stochastic gradient algorithm. This more commonly called the least mean square (LMS) algorithm.

12. Convergence rate of LMS is fast. State whether True or False.

a) True

b) False

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Explanation: The convergence rate of the LMS algorithm is slow. It is slow due to the fact that it uses only one parameter i.e. step size that control the adaptation rate.

13. 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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Explanation: Recursive least square (RLS) algorithm uses fast convergence rate as opposed to LMS algorithms. They are powerful, albeit complex, adaptive signal processing techniques which significantly improves the convergence of adaptive equalizer.

14. Which of the following algorithm uses simple programming?

a) LMS Gradient DFE

b) FTF algorithm

c) Fast Kalman DFE

d) Gradient Lattice DFE

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Explanation: Advantages of LMS gradient DFE algorithm are low computational complexity and simple programming. While fast tranversal filter (FTF) algorithm, Fast Kalman DFE and gradient lattice DFE uses complex programming.

**Sanfoundry Global Education & Learning Series – Wireless & Mobile Communications.**

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