4.5 equalizers and its types

4.5 equalizers and its types

1. EQUALIZERS
AND ITS
TYPES

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3. Introduction
•Inter symbol interference is the major problem in wireless
communication which leads to the BIT ERRORS at the receiver.
•Equalization is a technique used to reduce the inter symbol
interference.
•This device equalizes the dispersive effect of the channel.
(dispersion due to fading)
•Equalizers are mostly used at the receiver side.

4. Classification of equalizers.

5. Types of Equalizers
•Linear equalizers:
•If the output is not used in the feed back path to adapt
the equalizer is called linear equalizer.
•Non linear equalizers:
•If the output is fed back to change the subsequent
outputs of the equalizer is called as non linear
equalizers.
•Adaptive Equalizer:
•Anadaptive equalizeris anequalizerthat
automatically adaptsto time-varying properties of
thecommunication channel

6. LINEAR EQUALIZERS
•They are simple and resembles the filter structures.
•The product of the transfer function of the channel and equalizer
must satisfy certain criteria.
•The criteria can be,
•Either, Achieving a completely flat transfer function of the channel –filter
concatenation.
•Or, Minimizing the mean square error at the filter output.
•The basic structure of the linear equalizer is shown in the
figure.

7. •C
iTransmit Sequence sent over the channel.
•U
iSequence available at the Equalizer input.
•Now we have to convert the C
ito C
^
i.
•The aim of this conversion is to produce ZERO Deviation.
OR
•To produce minimum mean square error.

8. Types of Linear Equalizers
•There are 2 types of linear equalizers, they are:
•Zero Forcing Equalizer (ZF)
•Minimum Mean Square Error Equalizer (MMSE)

9. Zero Forcing Equalizers Vs MMSE Equalizers

10. Merits and demerits
•Merits
•Simple and easy to implement
•It has faster convergence
•Unique structure
•When channel becomes more time dispersive, the length of the equalizer
can be increased.
•Demerits
•Structure is complicated than compared to a linear equalizer.
•Not suitable for severe distortion channels.

11. 2. MMSE Equalizers
•In MMSE the ultimate aim is to reduce the BER but not the ISI.
•This can be achieved by minimizing the mean square error
between the signals.
•For minimizing the error the coefficients are found first.

12. NON LINEAR EQUALISERS
•These types of equalizers are used in applications
where the channel distortion is too severe for
linear equalizer to handle
•Linear equalizers are not suitable for the channels
which have deep spectral nulls in the passband
•There are various methods of Non Linear
Equalization, as follows
•Decision Feedback Equalization (DFE)
•Maximum Likelihood Symbol Detection
•Maximum Likelihood Sequence Estimation (MLSE)

13. Decision Feedback Equalizers

14. Maximum likelihood equalizer

16. Adaptive Equalizer

17. Adaptive Equalizers

18. Algorithms For Equalizers
•Least Mean Square -LMS
•Recursive Least Square -RLS

19. LMS
Algorithm

20. RLS Algorithms
•No assumptions are made in general
•Each signal is received individually and then they are analyzed for
the type of dispersion.
•This is more advantageous than the LMS alg.

21. Performance of an Algorithm
•The performance of the algorithm is determined by the various
factors
•Rate of convergence
•Misadjustment
•Computational Complexity
•Numerical Properties