Kamran FaisalShahid Iqbal

Haider Ali

▸ Overview▸ Why Equalization?▸ What is Equalization?

▸ Equalization Types▸ Equalization Algorithms

▸ Conclusion

2

Contents Include

“In the middle of every difficulty lies opportunity”.

“A person who never made a mistake never tried anything new”.

“You never fail until you stop trying”.

Albert Einstein

3

Overview!4

Why Equalization?

5

Why Equalization?

▸ Growth in communication services

▹ Satellite and fiber optics.

▹ High bandwidth

▹ Variety of data

▸ Require transmission technique over communication

channels.

6

What is Equalization! Process of adjusting the

balancce between frequency components.

Mitigate the effects of ISI (Inter Symbol Interference) Co-Channel Intrference Adjacent Channel Interference MultiPath Propagtion

7

What is Equalization!

Used to remove ISI and noise effects from the channel.

8

What is ISI?

9

What is ISI?

▸ Form of distortion of a signal

▹ One symbol interference with subsequent signals.

▸ Causes the errors at decision making device.

▸ Arises when data transmitted through the channel is

dispersive

▹ Received pulse is affected by the adjacent pulses

10

What is CCI?

11

What is CCI?

▸ Occurs in cellular radio system

▸ Caused by

▹ Adverse Weather Conditions

▹ Poor Frequency planning

▹ Daytime vs Nighttime

12

What is ACI?

13

What is ACI?

▸ Occurs in cellular radio system

▸ Caused by

▹ Extraneous power

▹ Inadequate filtering

▹ Improper tuning

14

Equalization Types

15

Equalization Types

▸ Linear Equalizer

▹ Process the incoming signal with a linear filter

▸ Decision Feedback Equalizer

▹ Non-Linear equalizer that uses the previous detector decision to eliminate the

ISI on modulated pulses .

▸ Blind Equalizer

▹ Estimates the transmitted signal without knowledge of the channel statistics,

using only knowledge of the transmitted signal’s statistics.

16

Equalization Algorithms

17

Equalization Algorithms

▸ LMS (Least Mean Square)

▹ Read the data signal.

▹ Calculate length of the data signal.

▹ Generate random noise and add it to data signal.

▹ Apply LMS Algorithm on noisy data signal.

▹ Filter output is y(n) = w(n) * x(n) where w(n) weight vector and x(n) is noise

data signal.

▹ Error e(n) = d(n) – y(n) Where d(n) is part of original data signal.

▹ Filter coefficient updating: w(n + 1) = w(n) + µx(n) e * (n)

▹ Update Filter Coefficient and minimize the error.

18

LMS AlgorithmPros & Cons

19

Advantage

Simplicity of implementation.

Not neglecting the noise like Zero Forcing Equalizer.

Stable and Robust performance against different signal conditions.

LMS Algorithm Pros and Cons

Disadvantage

Slow Convergence.

Decreasing the communication BW.

20

Conclusion

21

Conclusion

▸ Mandatory process for almost any modern digital

communication system.

▸ There is a variety of strategies

▹ Linear, Blind, etc.

▸ It is not a closed field, and it is subject to ongoing

research and improvements.

22

23

THANKS!Any questions?You can find me at▸ @username▸ user@mail.me