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:
• An adaptive equalizer is an equalizer that
automatically adapts to time-varying properties of
the communication 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. • Ci  Transmit Sequence sent over the channel.
• Ui  Sequence available at the Equalizer input.
• Now we have to convert the Ci to 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