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Channel Estimation for Mobile OFDM
Zhang Nan(62427P)
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OUTLINE
Basics of OFDM Channel Estimation Challenges of Channel Estimation in
Mobile OFDM Channel Estimation Techniques Performance Evaluation Conclusion
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OFDM Overview
Orthogonal Frequency Division Multiplexing To split a high-rate data stream into a number of lower rate st
reams that are transmitted simultaneously over a number of subcarrier
In OFDM systems, the spectrum of individual subcarrier is overlapped with minimum frequency spacing, which is carefully designed so that each subcarrier is orthogonal to the other subcarriers. The bandwidth efficiency of OFDM is another advantage.
In the guard time , the OFDM symbol is cyclically extended to avoid intercarrier interference.
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Advantages of OFDM
Immunity to delay spread Symbol duration >> channel delay spread Guard interval
Resistance to frequency selective fading Each subchannel is almost flat fading
Simple equalization Each subchannel is almost flat fading, so it only nee
ds a one-tap equalizer to overcome channel effect. Efficient bandwidth usage
The subchannel is kept orthogonality with overlap.
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Challenges of OFDM (1/2)
Synchronization Symbol synchronization
Timing errors Carrier phase noise
Frequency synchronization Sampling frequency synchronization Carrier frequency synchronization
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Challenges of OFDM (2/2)
High Peak to Average Power Ratio (PAPR) It increased complexity of the analog-to-di
gital and digital-to-analog converters. It reduced efficiency of the RF power amplif
ier.
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OFDM System Architecture
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Three Large Groups of Channel Estimation Techniques (1/3)
Channel estimation allows the receiver to approximate the effect of the channel on the signal.
Pilot Assisted It is the most straightforward way
where symbols or tones known to the receiver, called pilots.
Has a good performance in fast fading environments
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Three Large Groups of Channel Estimation Techniques (2/3)
Blind (without pilots) Based on channel statistics employment
rather than on that of pilots. No Training sequences required Most existing blind channel estimation
methods are based on second- or higher order statistics. It features relatively low complexity and a very fast convergence rate.
Hard to implement on real time systems.
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Three Large Groups of Channel Estimation Techniques (3/3)
Semi-Blind (with initial pilot-based channel estimation and next channel tracking) Assumes an intermediate position and
relies partly on pilots and partly on the use of channel statistics.
A semi-blind competitive neural network based method of time-varying channel estimation is tested in this work.
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Alogrithm
Consider a multipath radio channel. Assume the Jakes model on each path. CNN based channel estimator
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Competitive Neural Networks
One of the most famous self-organizing in the neural networks
A simple competitive network.
One common learning rule simply adds the difference between the winning neuron and the input sequence to the winning neuron.
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CNN Based OFDM Channel Estimator (1/2)
The winner neuron is selected according to the Kohonen updated rule
The dynamics of others neurons non-winners are defined as
)( 1,
1,,
kwn
kn
kwn
kwn NRNN
)( 1,,
1,,
kqn
kqn
kqn
kqn NRNN
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CNN Based OFDM Channel Estimator (2/2)
An estimate of the channel frequency response can be obtained from the weights of the neurons
4
1,
1
4
1ˆi
kin
ikn NjH
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Simulation Result
SNR=0
-800 -600 -400 -200 0 200 400 600 800-800
-600
-400
-200
0
200
400
600
800
1000
1200Received Neuron constellation
Real part
Imag
inar
y pa
rt
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Simulation Result
SNR=5
-400 -300 -200 -100 0 100 200 300-250
-200
-150
-100
-50
0
50
100
150
200
250Received Neuron constellation
Real part
Imag
inar
y pa
rt
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Simulation Result
SNR=10
-60 -40 -20 0 20 40 60 80-60
-40
-20
0
20
40
60Received Neuron constellation
Real part
Imag
inar
y pa
rt
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Simulation Result
SNR=15
-50 -40 -30 -20 -10 0 10 20 30 40-40
-30
-20
-10
0
10
20
30
40
50
60Received Neuron constellation
Real part
Imag
inar
y pa
rt
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Simulation Result
SNR=20
-10 -8 -6 -4 -2 0 2 4 6 8 10-10
-8
-6
-4
-2
0
2
4
6
8Received Neuron constellation
Real part
Imag
inar
y pa
rt
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Simulation Result
SNR=25
-4 -3 -2 -1 0 1 2 3 4 5-4
-3
-2
-1
0
1
2
3
4Received Neuron constellation
Real part
Imag
inar
y pa
rt
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Simulation Result
SNR=30
-1.5 -1 -0.5 0 0.5 1 1.5 2-1.5
-1
-0.5
0
0.5
1
1.5Received Neuron constellation
Real part
Imag
inar
y pa
rt
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MSE
The MSE measures the average of the square of the error which can be calculated as
N
iii HE
NMSE
1
2ˆˆ1
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MSE v.s. SNR
0 5 10 15 20 25 3010
-5
10-4
10-3
10-2
10-1
100
SNR in DB
Mea
n S
quar
ed E
rror
SNR V/S MSE