The document discusses channel estimation techniques for OFDM systems, highlighting the importance of dynamic channel estimation for effective demodulation due to the frequency selective and time-varying nature of radio channels. It covers block-type and comb-type pilot arrangements, including interpolation methods for estimating channels at data sub-carriers, and presents simulation results showing the performance of various estimation algorithms under different modulation schemes and fading conditions. The study, published in IEEE Transactions on Broadcasting, has been widely cited in the literature.

1. Channel Estimation Techniques
Based on Pilot Arrangement in
OFDM Systems
SINEM COLERI, MUSTAFA ERGEN, ANUJ PURI, AND AHMAD BAHAI
Presented By: Belal Essam Eldiwany
31 January 2017

2. Agenda
• Why Channel Estimation in OFDM Systems?
• System Description
• Channel Estimation Based on Block-Type Pilot Arrangement
• Channel Estimation Based on Comb-Type Pilot Arrangement
• Interpolation Techniques in Comb-Type Pilot Arrangement
• Simulations
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3. Before We Start …
• Published:
◦ in IEEE Transactions On Broadcasting,
◦ on September 2002.
• Till now, cited by 1617 other works.
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4. Why Channel Estimation in OFDM
Systems?
• A dynamic estimation of channel is necessary before the demodulation of
OFDM signals since the radio channel is frequency selective and time-varying
for wideband mobile communication systems.
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5. System Description
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6. System Description (Cont’d)
• The received signal is given by:
where h(n) is the channel impulse response and w(n) is Additive White Gaussian
Noise (AWGN).
• The channel response can be represented by [1]:
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7. System Description (Cont’d)
• Following DFT block, the pilot signals are extracted and the estimated channel
He(K) for the data sub-channels is obtained in channel estimation block.
• Then the transmitted data is estimated by:
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8. Channel Estimation Based on Block-Type
Pilot Arrangement
• In a periodic manner, all sub-
carriers are used as pilots for a
specific period.
• If the channel is constant during the
block, there will be no channel
estimation error since the pilots are
sent at all carriers.
• The estimation can be performed
by using either LS or MMSE.
Source: O Bataa, et al. “Interference Cancellation
Algorithm with Pilot in 3GPP/LTE ”31 January 2017

9. Channel Estimation Based on Block-Type
Pilot Arrangement (Cont’d)
• Comparing to
and assuming zero ISI, the output after DFT can be expressed as:
Y(k)= X(k) H(k) + W(k), k=0, 1, …, N-1.
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10. Channel Estimation Based on Block-Type
Pilot Arrangement (Cont’d)
• Or in matrix form as:
Y = XH + W = XFh + W
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11. Channel Estimation Based on Block-Type
Pilot Arrangement (Cont’d)
• If the time domain channel vector h is Gaussian and uncorrelated with the
channel noise W, then the frequency domain MMSE estimate of is given from
[2] as:
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12. Channel Estimation Based on Block-Type
Pilot Arrangement (Cont’d)
• The LS estimate is represented by:
which minimizes
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13. Channel Estimation Based on Comb-Type
Pilot Arrangement
• Portion of the total sub-carriers are used
as pilot signals.
• The pilots are uniformly inserted
between sub-carriers.
• The estimate of the channel at pilot sub-
carriers based on LS estimation is given
by:
Where Yp(k) and Xp(k) are output and
input at the kth pilot sub-carrier
respectively.
Source: O Bataa, et al. “Interference Cancellation
Algorithm with Pilot in 3GPP/LTE ”31 January 2017

14. Interpolation Techniques in Comb-Type Pilot
Arrangement
• In order to estimate channel at data sub-carriers, an efficient interpolation
technique is necessary by using the channel information at pilot sub-carriers.
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15. Interpolation Techniques in Comb-Type
Pilot Arrangement (Cont’d)
• Linear interpolation
For any data sub-carrier, form a linear combination of the preceding and following pilot sub-
carrier with proper weights.
• Second-order interpolation
Form a second order polynomial using three estimated pilot sub-carriers.
• The spline cubic interpolation
Produces a smooth and continuous polynomial fitted to given data points.
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16. Interpolation Techniques in Comb-Type Pilot
Arrangement (Cont’d)
• Low-pass interpolation
Performed by inserting zeros into the original sequence and then applying a low-pass FIR
filter that allows the original data to pass through unchanged and interpolates between such
that the mean-square error between the interpolated points and their ideal values is
minimized.
• Time domain interpolation
A high-resolution interpolation based on zero-padding and DFT/IDFT [8].
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17. Least Mean Squares Estimator
• The LMS estimator uses one tap LMS adaptive filter at each pilot frequency.
• The first value is found directly through LS and the following values are
calculated based on the previous estimation and the current channel output
as shown next
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18. Simulations
• BER performance of channel estimation algorithms for BPSK modulation in
Rayleigh fading channel.
Block-Type
- 30 symbols/block
- Pilots at first symbol.
‘block type ’ uses LS
estimate.
‘decision feedback’ uses
LS estimate with decision
feedback.
Comb-Type
- Pilot ratio is 1/8
(16 pilots).
‘linear, second-order,
low-pass, spline, time
domain’ use LS
estimate.
‘LMS’ uses LMS
estimate and linear
interpolation.
# sub-carriers= 128
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19. Simulations (cont’d)
• BER performance of channel estimation algorithms for DQPSK modulation in
Rayleigh fading channel.
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20. Simulations (cont’d)
• BER performance of channel estimation algorithms for 16-QAM modulation in
Rayleigh fading channel.
The block-type estimation and
decision feedback BER is 10–15
dB higher than that of the comb-
type estimation type.
 This is because the channel
transfer function changes so fast
that there are even changes for
adjacent OFDM symbols.
The comb-type pilot
arrangement allows the tracking
of fast fading channels.
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21. Simulations (cont’d)
• performance of channel estimation methods for 16-QAM modulation, Rayleigh
fading channel and 40 dB SNR for different Doppler frequencies.
BER increases as the
Doppler spread increases.
The reason is the
existence of severe ICI
caused by Doppler shifts.
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22. References
• [1] R. Steele, Mobile Radio Communications. London, England: Pentech Press
Limited, 1992.
• [2] O. Edfors, M. Sandell, J. J. van de Beek, S. K. Wilson and P. Ola Borjesson,
"OFDM channel estimation by singular value decomposition," Proceedings of
Vehicular Technology Conference - VTC, Atlanta, GA, 1996.
• [3]Yuping Zhao and Aiping Huang, "A novel channel estimation method for
OFDM mobile communication systems based on pilot signals and transform-
domain processing," 1997 IEEE 47th Vehicular Technology Conference.
Technology in Motion, Phoenix, AZ, 1997.
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23. Questions?
____________________________________________
Thank You
31 January 2017