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 Particle Filtering for Joint Data-Channel
Estimation in Fast Fading Channels 

• Tanya BERTOZZI

Didier Le Ruyet, Gilles Rigal and Han Vu-Thien
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Outline

• Problem statement

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Problem statement
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Problem Statement
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Classical solutions Slow fading
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Classical solutions Slow fading
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Classical solutions Fast fading
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Classical solutions Fast fading
PSP approach
Particle Filtering?
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Joint data-channel estimation applying the
Particle Filtering
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Particle filtering Joint data-channel estimation
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Particle filtering Joint data-channel estimation
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Particle filtering Joint data-channel estimation
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Particle filtering Joint data-channel estimation
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Particle filtering Joint data-channel estimation
The channel estimation
Along each trajectory in the state space the
channel is estimated by a Kalman filter.
I ) Prediction phase
II ) Correction phase
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Particle filtering Joint data-channel estimation
Calculation of the importance function
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Particle filtering Joint data-channel estimation
Calculation of the importance weights
Normalisation of the importance weights
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Particle filtering Joint data-channel estimation
Resampling
I ) Periodic every L bits
The particles with a weight lt T are moved in the
group with maximum weight.
II ) Uniformly according to the importance
weights
If
the particles are distributed uniformly
according to the importance weights.
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Particle filtering Joint data-channel estimation
Alternative scheme (E. Punskaya, A. Doucet, W.J.
Fitzgerald, EUSIPCO, September 2002)
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At each time only the best M particles are
retained
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close to the M algorithm
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k1
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Simulation results

• GSM system the receiver detects only one slot
  for each
• TDMA frame

• Preamble 26 known bits for the channel
  initialisation
• Information bits 58

• First channel model

• Second channel model HT240

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Simulation results
Comparison PSP-Particle filtering
First channel model FER versus Eb/No
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Simulation results
First channel model Complexity versus Eb/No
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Simulation results
HT240 FER versus Eb/No
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Simulation results
HT240 Complexity versus Eb/No
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Simulation results
Comparison M-T-Particle filtering
First channel model FER versus Eb/No
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Simulation results
First channel model Complexity versus Eb/No
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Preliminary conclusion
If the state space is discrete, the particle
filtering technique is equivalent to the
classical solutions.
When is it interesting to use the particle
filtering in digital communications?
Joint estimation of discrete and continuous
parameters
Example Joint delay-channel-data estimation
in DS-CDMA systems.
(The paper of Punskaya, Doucet and Fitzgerald
reaches the same conclusion)
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Joint delay-channel estimation in a DS-CDMA system
Data sequence
Spreading sequence
Chip duration
Received signal
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DS-CDMA Joint delay-channel estimation
State model
Channel
Delay
Nearly constant channel coefficients and constant
delay
Channel estimation
Kalman filter
Delay estimation
SISR algorithm
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DS-CDMA Joint delay-channel estimation
SISR algorithm for the delay estimation

• Initial distribution of the particles

• Selection of the importance function

• Calculation of the importance weights

• Resampling

uniformly according to the importance weights if
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Simulation results
Time
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Simulation results
Time
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Conclusion
Possible applications of the PF in digital
communications
Discrete state space
equivalent to the classical solutions (M and T
algorithms)
More interesting
PF for the joint estimation of discrete and
continuous parameters
Example Joint delay-channel estimation in a
DS-CDMA system
The first results are encouraging this approach
can give better performance than the classical
solutions.