equalization (technique on receiver side to remove interferences)
TRANSCRIPT
Kamran FaisalShahid Iqbal
Haider Ali
▸ Overview▸ Why Equalization?▸ What is Equalization?
▸ Equalization Types▸ Equalization Algorithms
▸ Conclusion
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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
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Overview!4
Why Equalization?
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Why Equalization?
▸ Growth in communication services
▹ Satellite and fiber optics.
▹ High bandwidth
▹ Variety of data
▸ Require transmission technique over communication
channels.
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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
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What is Equalization!
Used to remove ISI and noise effects from the channel.
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What is ISI?
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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
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What is CCI?
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What is CCI?
▸ Occurs in cellular radio system
▸ Caused by
▹ Adverse Weather Conditions
▹ Poor Frequency planning
▹ Daytime vs Nighttime
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What is ACI?
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What is ACI?
▸ Occurs in cellular radio system
▸ Caused by
▹ Extraneous power
▹ Inadequate filtering
▹ Improper tuning
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Equalization Types
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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.
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Equalization Algorithms
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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.
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LMS AlgorithmPros & Cons
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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.
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Conclusion
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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.
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THANKS!Any questions?You can find me at▸ @username▸ [email protected]