Chapter Two Mitigation Techniques
Content
Chapter Two
Mobile Radio Channel Modelling & Mitigations
2.2 Mitigation Techniques for Fading Wireless Channels
By : Amare Kassaw
Goal of the Lecture
Radio channel is dynamic because of multipath fading and
Doppler spread
Fading cause the signal at the receiver to fade
How to improve link performance in hostile mobile environment.
Apart from better transmitter and receiver technology, mobile
communications require signal processing techniques that
improve the link performance
Mitigation techniques: Channel equalization, diversity, spread
spectrum, interleaving, channel coding,
Lecture Outlines
Introduction
Equalization Techniques
Diversity Techniques
Coding Techniques
Summery
Used Acronyms
•
•
•
•
•
•
DFE : Decision feedback equalizer
ISI: Inter symbol interference
FTF: Fast transversal filter
LMS : least mean square
ZF: Zero forcing
RLS: Recursive least square
Introduction
Mobile radio channel is particularly dynamic due to
Multipath fading
Doppler spread
As a result, the channel has a strong negative impact on BER of
any modulation and transmission techniques
To improve received signal quality in hostile mobile radio
environment, we need
Equalization
Diversity
Channel coding, ..
Each can be used independently or in tandem
Equalization: compensates for inter symbol interference (ISI)
created by multipath in time dispersive(frequency selective )
channels
Recall pulse shaping filters that also compensate for ISI
ISI is the result of frequency selective channel
Equalizers must be adaptive since the channel is generally
unknown and time varying
It may be linear equalization or nonlinear equalizer
Diversity: compensates for fast fading channel impairments
It is employed to reduce the depth and duration of the fades
experienced by a receiver
Idea: create independent (or at least highly uncorrelated) signal
“channels” for communication
Types of diversity:
Spatial diversity, Frequency diversity, Time diversity,
Polarization diversity
Spatial diversity: usually implemented by using two or more
receiving antennas and widely used
Channel Coding: improves mobile communication link
performance by adding redundant data bits in the transmitted
message
It is used by the Rx to detect or correct some (or all) of errors
introduced by the channel in a particular sequence of message bits
(fading or noise).
Post detection technique
Examples: Block codes and convolutional codes
A general framework of fading effects and their mitigation
techniques.
Equalization Techniques
ISI is one of the major obstacles to high speed data transmission
over mobile radio channels.
If BS>BC of the radio channel (frequency selective fading),
modulated pulses are spread in time, causing ISI.
An equalizer at the front end of a receiver compensates for the
average range of expected channel amplitude and delay
characteristics.
Equalizers must track the time-varying characteristics of the
mobile channel and therefore should be time varying or
adaptive.
Equalizers are widely used in TDMA systems
Three factors affect the time span over which an equalizer
converges:
Equalizer algorithm, equalizer structure, and time rate of change
of multipath radio channel
Two operating modes for an adaptive equalizer are:
Training mode
Tracking mode
Adaptive equalizer training mode operation:
Initially a known fixed length training sequence is sent by the
Tx so that the Rx equalizer may average to a proper setting.
Training sequence is typically a pseudo-random binary signal or
a fixed prescribed bit pattern.
The training sequence is designed to permit an equalizer at the
receiver to acquire the proper filter coefficient in the worst
possible channel condition.
An adaptive filter at the receiver thus uses a recursive algorithm
to evaluate channel and estimate filter coefficients to
compensate for the channel.
Adaptive equalizer tracking mode operation:
When the training sequence is finished the filter coefficients
are near optimal.
Immediately following the training sequence, user data is sent.
When the data of the users are received, the adaptive
algorithms of the equalizer tracks the changing channel.
As a result, the adaptive equalizer continuously changes the
filter characteristics over time.
Mathematical Frame Work of an Equalizer
Equalizer is usually implemented at baseband or at IF in a
receiver
The signal received by the equalizer is given by
If the impulse response of the equalizer is heq(t), the output of
the equalizer is
Ῡ(t) = d (t) * h (t) * heq (t) + nb (t) * heq (t) = d (t)* g (t) + nb(t) * heq (t)
With nb(t) equal to zero, to be y(t)=d(t),
Hence the main goal of any equalization process is to satisfy this
equation optimally.
In frequency domain it can be written as
Thus an equalizer is actually an inverse filter of the channel
For frequency selective channel: to provide a flat composite
received frequency response and linear phase response;
The equalizer enhances the frequency components with small
amplitudes
Attenuates the strong frequencies in the received frequency
spectrum
For time varying channel: the equalizer is designed to track the
channel variations so that the above equation is approximately
satisfied.
Generic Adaptive Equalizer:
Basic Structure : Transversal filter with N delay elements, N+1
taps, and N+1 tuneable complex weights.
Weights are updated continuously by an adaptive algorithm
The adaptive algorithm is controlled by the error signal ek: Fig
An adaptive equalizer is a time-varying filter that is retuned
constantly
In the block diagram:
The subscript k represents discrete time index
There is a single input yk at any time instant
It is a transversal filter that has N delay, N+1 taps and N+1
tuneable multiplier called weights
The value of yk depends upon
Instantaneous state of radio channel and specific value of
noise
The second subscript( k) of the weights show that they vary with
time and are updated on a sample by sample basis
The error signal ek
Controls the adaptive algorithm
The error signal is derived by comparing the output of the
equalizer with some signal dk which is either
Replica of transmitted signal xk or
Which represents a known property of the transmitted signal
ek is used to minimize a cost function and iteratively update
equalizer weights so as to reduce the cost function
The Least Mean Square (MSE) algorithm searches for the
optimum or near optimum weight by
Computing the error between the desired signal and the
output of the equalizer and minimizes it
It is the most common cost function
Adaptive Equalization Classification
Used to mitigate more
severe fading channel
Performance measures for an adaptive algorithm
Rate of convergence
Mis-adjustment
Computational complexity and numerical properties
Factors that dominate the choice of an equalization structure and
its algorithm
The cost of computing platform
The power budget
The radio propagation characteristics
Algorithms types
Zero Forcing (ZF)
Least Mean Squares (LMS)
Recursive least square (RLS)
The speed of the mobile unit determines the channel fading rate
and the Doppler spread
Which is related to the coherent time of the channel directly
The choice of adaptive algorithm, and its corresponding rate of
convergence, depends on the channel data rate and coherent time
The number of taps used in the equalizer design depends on the
maximum expected time delay spread of the channel
The circuit complexity and processing time increases with the
number of taps and delay elements
Diversity Techniques
Diversity exploits the random nature of radio propagation by
finding independent (or at least highly uncorrelated) signal
“channels or paths” for communication
Idea: “don’t put all of your eggs in one basket”
In fading channels, a signal power will fall below any given
fade margin at finite probability exists
Send copies of a signal using multiple channels
Time, frequency, space, antenna
If one radio path undergoes a deep fade, another independent
path may have a strong signal
Assumption: Individual channels experience independent fading
events
By having more than one path to select from, SNR at a receiver
may be improved (by as much as 20 to 30 dB). Figure
Advantage: Diversity requires no training overhead
It provides significant link improvement with little added cost
Assume that we have M statistically independent channels
• This independence means that one channel’s fading does not
influence, or is not correlated with, another channel’s fading
Examples: Using antenna (or space) diversity
Microscopic diversity: Mitigates small-scale fading effects
(deep fading)
Macroscopic diversity: Reduces the large-scale fading
(selecting different base stations), can also be used for uplink
• Selecting an antenna which is not shadowed
Types of Diversity
Time diversity
Repeatedly transmits information at time spacing that exceed
the coherence time of the channel, e..g., interleaver
Spreading the data out over time & better for fast fading
channel
Frequency diversity
Transmits information on more than one carrier frequency
Frequencies separated by more than the coherence bandwidth
of the channel will not experience the same fads (eg., FDM)
Also spread spectrum (spread the signal over a larger frequency
bandwidth) or OFDM (use multiple frequency carriers)
Used to mitigate the frequency selective fading channel
Figure . Frequency diversity
Space diversity
Transmit information on spatially uncorrelated channels
Requires multiple antennas at transmitter and/or receiver
• Example: MIMO, SIMO, MISO, virtual antenna systems
Multipath fading changes quickly over space
• Hence, the signal amplitude received on the different
antennas can have a low correlation coefficient
Space diversity doesn't require additional
Rx
Tx
λ/2
• Signals to be transmitted
• Bandwidth for transmission
λ/2
Reception methods for space diversity includes:
• Selection combining
• Maximal-ratio combining
• Equal gain combining
Selection Combining:
The receiver branch, having the highest instantaneous SNR, is
connected to the demodulator
The antenna signals themselves could be sampled and the best
one sent to a single demodulation
Simple to implement but does not use all of the possible
branches
Generalized receiver block diagram for selection diversity
Example: See Handout
Maximum Ratio Combining
The received signals are weighted with respect to their SNR
and then summed
Principle: Combine all the signals from all of the M branches
in a co-phased and weighted manner so as to have the highest
SNR at the receiver at all times
The control algorithms for setting the gains and phases for
MRC are similar to those required in equalizer
Need time to converge & performance is as good as the
channel
Generalized receiver block diagram for MRC
Equal Gain Combining:
In equal gain combining
The branch weights are all set to unity but the signals from
each are co-phased to provide equal gain combining diversity
Co-phased signals are then add together
All the received signals are summed coherently.
This allows the receiver to exploit signals that are
simultaneously received on each branch
In certain cases, it is not convenient to provide for the variable
weighting capability as in MRC
The probability of producing an acceptable signals from a
number of unacceptable inputs is still retained
The performance is marginally inferior to maximal ratio
combining and superior to selection combining
Figure : Equal Gain Combining
Channel Coding Techniques
It is used by the Rx to detect or correct some (or all) of the errors
introduced by the channel (Post detection technique)
It improves mobile communication link performance by adding
redundant data bits in the transmitted message
Mainly for error control and classified as block or convolutional
codes
Block Codes: examples
• FEC codes, Hamming Codes, Hadamard Codes
• Golay Codes, Cyclic Codes, BCH cyclic, Reed-Solomon Codes
Convolutional codes: Here the output of the FEC encoder can
be viewed as the convolution of the input bit stream and the
impulse response of the encoder. Which is a time invariant
polynomial.
A convolutional code is described by a set of rules by which the
encoding of k data bits into n-coded data (n, k)
The ratio of k/n is typically called the code rate, this ratio
determines the amount of additional redundancy inserted into the
code word.
The smaller the code rate the more parity bits are inserted into the
data stream.
Conclusion
Equalizers attempt to make the discrete time impulse response of
the channel ideal
Channels act as filters that cause both amplitude and phase
distortion of signals
Transmitters and receivers can be designed as filters to compensate
for non-ideal channel behaviour
Training sequences can be used to adapt equalizer weights
Multiple techniques are available for setting filter tap weights
Zero forcing
Least mean squares
Recursive least squares
Diversity is one technique to combat fading in wireless channel
Time diversity: Used when channels spacing is greater than the
coherence time of the channel
Repeating transmission in time correlated channel brings
little advantage
Good with fast fading channels
Frequency diversity: used when channels frequency separation
is greater than the coherence bandwidth of the channel
Spatial diversity requires multiple antennas
E.g., MIMO and virtual antenna systems
Finally channel coding is mainly used for error control
Mobile Radio Channel Modelling & Mitigations
2.2 Mitigation Techniques for Fading Wireless Channels
By : Amare Kassaw
Goal of the Lecture
Radio channel is dynamic because of multipath fading and
Doppler spread
Fading cause the signal at the receiver to fade
How to improve link performance in hostile mobile environment.
Apart from better transmitter and receiver technology, mobile
communications require signal processing techniques that
improve the link performance
Mitigation techniques: Channel equalization, diversity, spread
spectrum, interleaving, channel coding,
Lecture Outlines
Introduction
Equalization Techniques
Diversity Techniques
Coding Techniques
Summery
Used Acronyms
•
•
•
•
•
•
DFE : Decision feedback equalizer
ISI: Inter symbol interference
FTF: Fast transversal filter
LMS : least mean square
ZF: Zero forcing
RLS: Recursive least square
Introduction
Mobile radio channel is particularly dynamic due to
Multipath fading
Doppler spread
As a result, the channel has a strong negative impact on BER of
any modulation and transmission techniques
To improve received signal quality in hostile mobile radio
environment, we need
Equalization
Diversity
Channel coding, ..
Each can be used independently or in tandem
Equalization: compensates for inter symbol interference (ISI)
created by multipath in time dispersive(frequency selective )
channels
Recall pulse shaping filters that also compensate for ISI
ISI is the result of frequency selective channel
Equalizers must be adaptive since the channel is generally
unknown and time varying
It may be linear equalization or nonlinear equalizer
Diversity: compensates for fast fading channel impairments
It is employed to reduce the depth and duration of the fades
experienced by a receiver
Idea: create independent (or at least highly uncorrelated) signal
“channels” for communication
Types of diversity:
Spatial diversity, Frequency diversity, Time diversity,
Polarization diversity
Spatial diversity: usually implemented by using two or more
receiving antennas and widely used
Channel Coding: improves mobile communication link
performance by adding redundant data bits in the transmitted
message
It is used by the Rx to detect or correct some (or all) of errors
introduced by the channel in a particular sequence of message bits
(fading or noise).
Post detection technique
Examples: Block codes and convolutional codes
A general framework of fading effects and their mitigation
techniques.
Equalization Techniques
ISI is one of the major obstacles to high speed data transmission
over mobile radio channels.
If BS>BC of the radio channel (frequency selective fading),
modulated pulses are spread in time, causing ISI.
An equalizer at the front end of a receiver compensates for the
average range of expected channel amplitude and delay
characteristics.
Equalizers must track the time-varying characteristics of the
mobile channel and therefore should be time varying or
adaptive.
Equalizers are widely used in TDMA systems
Three factors affect the time span over which an equalizer
converges:
Equalizer algorithm, equalizer structure, and time rate of change
of multipath radio channel
Two operating modes for an adaptive equalizer are:
Training mode
Tracking mode
Adaptive equalizer training mode operation:
Initially a known fixed length training sequence is sent by the
Tx so that the Rx equalizer may average to a proper setting.
Training sequence is typically a pseudo-random binary signal or
a fixed prescribed bit pattern.
The training sequence is designed to permit an equalizer at the
receiver to acquire the proper filter coefficient in the worst
possible channel condition.
An adaptive filter at the receiver thus uses a recursive algorithm
to evaluate channel and estimate filter coefficients to
compensate for the channel.
Adaptive equalizer tracking mode operation:
When the training sequence is finished the filter coefficients
are near optimal.
Immediately following the training sequence, user data is sent.
When the data of the users are received, the adaptive
algorithms of the equalizer tracks the changing channel.
As a result, the adaptive equalizer continuously changes the
filter characteristics over time.
Mathematical Frame Work of an Equalizer
Equalizer is usually implemented at baseband or at IF in a
receiver
The signal received by the equalizer is given by
If the impulse response of the equalizer is heq(t), the output of
the equalizer is
Ῡ(t) = d (t) * h (t) * heq (t) + nb (t) * heq (t) = d (t)* g (t) + nb(t) * heq (t)
With nb(t) equal to zero, to be y(t)=d(t),
Hence the main goal of any equalization process is to satisfy this
equation optimally.
In frequency domain it can be written as
Thus an equalizer is actually an inverse filter of the channel
For frequency selective channel: to provide a flat composite
received frequency response and linear phase response;
The equalizer enhances the frequency components with small
amplitudes
Attenuates the strong frequencies in the received frequency
spectrum
For time varying channel: the equalizer is designed to track the
channel variations so that the above equation is approximately
satisfied.
Generic Adaptive Equalizer:
Basic Structure : Transversal filter with N delay elements, N+1
taps, and N+1 tuneable complex weights.
Weights are updated continuously by an adaptive algorithm
The adaptive algorithm is controlled by the error signal ek: Fig
An adaptive equalizer is a time-varying filter that is retuned
constantly
In the block diagram:
The subscript k represents discrete time index
There is a single input yk at any time instant
It is a transversal filter that has N delay, N+1 taps and N+1
tuneable multiplier called weights
The value of yk depends upon
Instantaneous state of radio channel and specific value of
noise
The second subscript( k) of the weights show that they vary with
time and are updated on a sample by sample basis
The error signal ek
Controls the adaptive algorithm
The error signal is derived by comparing the output of the
equalizer with some signal dk which is either
Replica of transmitted signal xk or
Which represents a known property of the transmitted signal
ek is used to minimize a cost function and iteratively update
equalizer weights so as to reduce the cost function
The Least Mean Square (MSE) algorithm searches for the
optimum or near optimum weight by
Computing the error between the desired signal and the
output of the equalizer and minimizes it
It is the most common cost function
Adaptive Equalization Classification
Used to mitigate more
severe fading channel
Performance measures for an adaptive algorithm
Rate of convergence
Mis-adjustment
Computational complexity and numerical properties
Factors that dominate the choice of an equalization structure and
its algorithm
The cost of computing platform
The power budget
The radio propagation characteristics
Algorithms types
Zero Forcing (ZF)
Least Mean Squares (LMS)
Recursive least square (RLS)
The speed of the mobile unit determines the channel fading rate
and the Doppler spread
Which is related to the coherent time of the channel directly
The choice of adaptive algorithm, and its corresponding rate of
convergence, depends on the channel data rate and coherent time
The number of taps used in the equalizer design depends on the
maximum expected time delay spread of the channel
The circuit complexity and processing time increases with the
number of taps and delay elements
Diversity Techniques
Diversity exploits the random nature of radio propagation by
finding independent (or at least highly uncorrelated) signal
“channels or paths” for communication
Idea: “don’t put all of your eggs in one basket”
In fading channels, a signal power will fall below any given
fade margin at finite probability exists
Send copies of a signal using multiple channels
Time, frequency, space, antenna
If one radio path undergoes a deep fade, another independent
path may have a strong signal
Assumption: Individual channels experience independent fading
events
By having more than one path to select from, SNR at a receiver
may be improved (by as much as 20 to 30 dB). Figure
Advantage: Diversity requires no training overhead
It provides significant link improvement with little added cost
Assume that we have M statistically independent channels
• This independence means that one channel’s fading does not
influence, or is not correlated with, another channel’s fading
Examples: Using antenna (or space) diversity
Microscopic diversity: Mitigates small-scale fading effects
(deep fading)
Macroscopic diversity: Reduces the large-scale fading
(selecting different base stations), can also be used for uplink
• Selecting an antenna which is not shadowed
Types of Diversity
Time diversity
Repeatedly transmits information at time spacing that exceed
the coherence time of the channel, e..g., interleaver
Spreading the data out over time & better for fast fading
channel
Frequency diversity
Transmits information on more than one carrier frequency
Frequencies separated by more than the coherence bandwidth
of the channel will not experience the same fads (eg., FDM)
Also spread spectrum (spread the signal over a larger frequency
bandwidth) or OFDM (use multiple frequency carriers)
Used to mitigate the frequency selective fading channel
Figure . Frequency diversity
Space diversity
Transmit information on spatially uncorrelated channels
Requires multiple antennas at transmitter and/or receiver
• Example: MIMO, SIMO, MISO, virtual antenna systems
Multipath fading changes quickly over space
• Hence, the signal amplitude received on the different
antennas can have a low correlation coefficient
Space diversity doesn't require additional
Rx
Tx
λ/2
• Signals to be transmitted
• Bandwidth for transmission
λ/2
Reception methods for space diversity includes:
• Selection combining
• Maximal-ratio combining
• Equal gain combining
Selection Combining:
The receiver branch, having the highest instantaneous SNR, is
connected to the demodulator
The antenna signals themselves could be sampled and the best
one sent to a single demodulation
Simple to implement but does not use all of the possible
branches
Generalized receiver block diagram for selection diversity
Example: See Handout
Maximum Ratio Combining
The received signals are weighted with respect to their SNR
and then summed
Principle: Combine all the signals from all of the M branches
in a co-phased and weighted manner so as to have the highest
SNR at the receiver at all times
The control algorithms for setting the gains and phases for
MRC are similar to those required in equalizer
Need time to converge & performance is as good as the
channel
Generalized receiver block diagram for MRC
Equal Gain Combining:
In equal gain combining
The branch weights are all set to unity but the signals from
each are co-phased to provide equal gain combining diversity
Co-phased signals are then add together
All the received signals are summed coherently.
This allows the receiver to exploit signals that are
simultaneously received on each branch
In certain cases, it is not convenient to provide for the variable
weighting capability as in MRC
The probability of producing an acceptable signals from a
number of unacceptable inputs is still retained
The performance is marginally inferior to maximal ratio
combining and superior to selection combining
Figure : Equal Gain Combining
Channel Coding Techniques
It is used by the Rx to detect or correct some (or all) of the errors
introduced by the channel (Post detection technique)
It improves mobile communication link performance by adding
redundant data bits in the transmitted message
Mainly for error control and classified as block or convolutional
codes
Block Codes: examples
• FEC codes, Hamming Codes, Hadamard Codes
• Golay Codes, Cyclic Codes, BCH cyclic, Reed-Solomon Codes
Convolutional codes: Here the output of the FEC encoder can
be viewed as the convolution of the input bit stream and the
impulse response of the encoder. Which is a time invariant
polynomial.
A convolutional code is described by a set of rules by which the
encoding of k data bits into n-coded data (n, k)
The ratio of k/n is typically called the code rate, this ratio
determines the amount of additional redundancy inserted into the
code word.
The smaller the code rate the more parity bits are inserted into the
data stream.
Conclusion
Equalizers attempt to make the discrete time impulse response of
the channel ideal
Channels act as filters that cause both amplitude and phase
distortion of signals
Transmitters and receivers can be designed as filters to compensate
for non-ideal channel behaviour
Training sequences can be used to adapt equalizer weights
Multiple techniques are available for setting filter tap weights
Zero forcing
Least mean squares
Recursive least squares
Diversity is one technique to combat fading in wireless channel
Time diversity: Used when channels spacing is greater than the
coherence time of the channel
Repeating transmission in time correlated channel brings
little advantage
Good with fast fading channels
Frequency diversity: used when channels frequency separation
is greater than the coherence bandwidth of the channel
Spatial diversity requires multiple antennas
E.g., MIMO and virtual antenna systems
Finally channel coding is mainly used for error control
