March 10, 2010
Channel Estimation Techniques for MB-OFDM UWB Systems (1/2)
Project Program-2: Wireless Broadband Access
Presentation # 01
Presented in the Class of
Professor Kyung Sup Kwak
Presented By:
S. M. Riazul Islam 리아즈
STD ID: 82081029
Graduate School of Information Technology and Telecommunications
Inha University, South Korea
Contents

Introduction

MB-OFDM UWB System

Modeling & RF Plan

OLA Channel Estimation

Time-frequency Channel Estimation

Pilot-based & Decision feedback

DFT-Based Channel Estimation

Adaptive Channel Estimation

Iterative Interpolation Channel Estimation
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
2
Introduction



potential for high data rate
low susceptibility to multi-path interference
UWB technology can enable a wide variety of applications in wireless
communications

One of the promising UWB technologies is multi-band orthogonal frequency division
multiplexing (MB-OFDM) [standard ECMA-368].

The MB-OFDM systems have the following unique characteristics if compared to
conventional OFDM systems

different channel responses and channel energies across different bands

different carrier frequency offsets (CFOs) across different bands

the use of zero padding (ZP) instead of cyclic prefix (CP)

interplay between the timing and the frequency hopping.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
3
Introduction

additional design constraints, and hence, should be taken into account in the designs
of synchronization, channel estimation and equalization


Multipath Channel>>Intersymbol Interference (ISI)
Mobility>>Intercarrier Interference (ICI)
Narrowband Interference (NBI)
Time-variation of Channel
Freq-variation of Channel
Channel Model
AWGN, Fading, Quasi-Static
Channel Impulse Response (CIR) is required in Rx side

Channel Estimation






March 10, 2010
Telecommunication Engineering Lab, INHA Univ
4
MB-OFDM System
UWB spectrum into 14 bands, each
with a bandwidth of 528 MHz.
The first 12 bands are then grouped
into 4 band groups consisting of 3
bands, and last two bands are
grouped into a fifth band group.
 A total of 110 sub-carriers (100 data
carriers and 10 guard carriers) are
used per band. In addition, 12 pilot
sub-carriers allow for coherent
detection
s (n, k ) 
1
N FFT
ND
NG
l 0
l 0
[ C D ,n [l ] exp( j 2M D [l ]k / N FFT )   C G ,n [l ] exp( j 2M G [l ] / N FFT )
Np
  C P ,n [l ] exp( j 2M P [l ] / N FFT )]    (1)
l 0
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
5
MB-OFDM System
Channel Model 802.15.3a
Lh
hRF  X 
l 0
K

k 0
k ,l
 (t  Tl   k ,l )    (2)
where Tl, τk,l and X are random variables (RVs) representing the delay (time arrival)
of l-th cluster, the delay (relative to l-th cluster arrival time) of the k-th multi-path
component of the l-th cluster, and the log-normal shadowing, respectively.
R(n, k )  H (n, k ).S (n, k )  W (n, k )    (3)
where W(n,k) is the frequency domain additive white Gaussian noise (AWGN) and
H(n,k) is the channel frequency response for the nth OFDM symbol.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
6
OLA Channel Estimation(1)
Ref: Y. Li, H. Minn and R. M. A. P. Rajatheva, “Synchroization, Channel Estimation and Equalization in MB-OFDM
Systems”, IEEE Transactions on Wireless communications, vol. 7, No. 11, pp. 4341-4352, November 2008.
developed by first averaging the over-lap-added (OLA) received preamble
symbols within the same band and then applying time-domain least-squares
method followed by the discrete Fourier transform.
Specifically, for every l-th nonzero OFDM symbol in the qth band after CFO
compensation, the OLA method adds M0 samples
^
^
^
{rq (k ft  lM  N ), rq (k ft  lM  N  1),..., rq (k ft  lM  M 0  N  1)}
^
rq (k ft  lM  k )
 ^
(l )
y q (k )   rq (k ft  lM  k  N ; if ,0  k  M 0  1
^
rq (k ft  lM  k ); else    (4)

to
^
^
^
{rq (k ft  lM ), rq (k ft  lM  1),..., rq (k ft  lM  M 0  1)}
CFO compensation for the qth band
^
rq (k ft  m)e
 j 2m v bq / N
 ^
 rq (k ft  m); for, m  0,1,...
fine timing offset; CFO estimator
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
7
OLA Channel Estimation(2)
After applying the OLA method, the preamble symbols in each band are
averaged as

1
yq 
Lq ,c
Lq , c
y
l 1
(l )
q
   (6)
is the number of non-zero OFDM preamble samples in the symbols of part-c
in the qth band
Finally, the application of the time-domain lease-square (LS) channel
estimator for the qth band provides
^
1
h q  (S S ) S
H
H

y q    (7)
where S be the low-pass-equivalent time-domain OFDM preamble samples.
Using this channel estimation different types of equalizers: one-tap zeroforcing (ZF) equalizer, minimum mean square error (MMSE) equalizer, onetap MMSE equalizer are developed.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
8
OLA Channel Estimation:Comments(3)



the best OLA window lengths for preamble and data are observed to be approximately the same
the use of OLA length larger than the best value gives a more robust performance than the use of
a smaller length
The MMSE equalizer is no longer one-tap frequency-domain equalization (FDE), but the later
gives almost the same performance as the former and hence is a better choice for practical
systems

pilot based but the derivation in (7) shows estimated channel is dependent on CFO compensation
that means estimated channel does not directly depends on channel covariance matrix

considers a number of noise-only sample followed by preamble samples since the receiver does
not know in advance when the preamble will arrive. They consider that the number of initial noiseonly samples is a RV depending on the time of arrival (TOA) of the preamble. Accordingly, this
technique performs some complex optimization algorithms which would be easily avoided (to
reduce the complexity further) by performing simple model based TOA estimation.

inadequate BER Performance to handle high rate services

Since complexity can be substantially reduced by using TOA estimation procedure, so the
application of the techniques in [ref] is inspired.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
9
Time-frequency Channel Estimation(1)
Ref: Florent Munier and Thomas Eriksson, “Time-Frequency Channel Estimation for MB-OFDM UWB Systems”,
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki,
Finland, 2006.
two steps process to develop the expected estimator.
In a first step, the time-frequency correlation properties of the channel are derived.
In the next step, authors derive an iterative MMSE channel estimation algorithm, adapted for a UWB
system using multi-band OFDM.
As an extension of the MMSE channel estimation they also propose iterative decision feedback
estimation.
Pilot-Based Estimation
An estimate of the channel coefficients is performed for each packet transmission.
p
define the vector D as containing all pilots symbols present in the packet. They also use Dmp (k ) to
denote a QPSK pilot symbol at the k-th sub-carrier.
The MMSE channel
estimator based on the
observation of the pilot vector
is a bank of wiener filter
March 10, 2010
an accounting for all pilots in the frame
^ p
p
H  C HR
C
T
p
C HR
 E[ HR p ]
p 1
RR
R p    (8)
T
p
C RR
 E[ R p R p ]
Telecommunication Engineering Lab, INHA Univ
*
R p  diag ( D p ) S p    (9)
10
Time-frequency Channel Estimation(2)
Decision Feedback Estimation
Once the channel estimate has been obtained with the pilots, they perform a preliminary symbol
decision and decoding on the packet payload.
These decode bits are then recodes and remapped to symbol candidates which are fed back to the
estimator.
At this point the estimation refined using decisions taken from the data to form a new vector Y F and
the decision feedback estimation (estimation is done iteratively.)
^ F
Comments
1
F
F
H  C HR
C RR
R F    (10)
motivated by the facts a) time-domain equalization (TDE) sees more complexity and becomes an
issue when the path density of the channel is high
b) On the other hand, FDE sees correlated flat fading of OFDM symbols by the channel, with pilot
based techniques using time-correlation between symbols.
Accordingly, this technique proposes an MMSE channel estimator exploiting both the time and
frequency correlation of the channel as well as iterative decision feedback to improve the quality of
the channel estimate.
In addition to this good motivation, the BER performance of this system is quite satisfactory as seen
from their simulation results.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
11
Time-frequency Channel Estimation(3)
>>Comments
they don’t consider the doubly selective nature of the channel, although they claim
that channel is time-frequency correlated
increase the complexity a lot because of iterative approach during tracking
authors assume the channel is free from ISI but it not true since to make ISI free
channel the length of CP or ZP must be long enough
varying nature of the channel when it jumps from one symbol to another,
considering ISI term in system modeling, applying some low complexity optimization
instead of iteration.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
12
DFT-Based Channel Estimation (1)
Ref: Yang Xiao-dong, Zhu Xiao-ming and Wang yu-dong, “Analysis of DFT-Based Channel Estimation Algorithm for
UWB-OFDM System”, IET International Conference on Wireless, Mobile and Multimedia Networks, Mumbai,
India, 2008.
Estimated channel is obtained through the collection of frequency domain CIR based on LS criterion
and then multiplying this CIR by Hanning window function. In addition, multiplication is performed in
time domain to avoid spectral leakage
Hence, as a first step, LS estimation expression using pilots becomes
^
H p  ( S p ) 1 R p    (11)
this estimated channel is time windowed as
^ p
h (i)  h (i).d (i), where; i  0,1,..., M  1;  (12)
p
d
where d(i) is the Hanning window function of length M (say
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
13
DFT-Based Channel Estimation (2)
Comments
very simple and does not increase any computational complexity.
Besides, because of applying a generalized Hanning window function it makes channel frequency
response could gradually transit to zero; so that frequency spectrum augments stop-band attenuation for
reducing spectrum leakage and improving estimation accuracy.
this technique does not compare the performances of other windowing functions for OFDM-based
UWB system.
Blackman window function has the best spectral leakage reduction capability
Other window functions which have good spectral leakage reduction capability are Flat top, Kaiser,
Hamming etc.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
14
Adaptive Channel Estimation (1)
Ref: Khiam-Boon Png, Xiaoming peng and Francois Chin, “A Low Complexity Adaptive Channel
Estimation Scheme for MB-OFDM System”, IEEE International Conference on Ultrawideband (ICUWB), Germany, 2008.
Estimating channel’s power delay profile (PDF).
A simple LS criterion is performed as an initial estimation. Then, estimate the PDF for each sub-band by
using the correlation of transmitted sequence with the received sequence over the remaining preambles
after symbol synchronization.
Pc ,q 

mod( i 1, 3) c
subcarriers
R
j 1
power for the qth delay path in the cth band
i. j
2
* S jp    (13)
jth transmitted preamble symbol
Next they generate the profile vector gc,q by performing maximization algorithm over PDF with a
heuristic threshold factor K.
^ p
H c  Gc  H c    (14)
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
15
Adaptive Channel Estimation (2)
Comments
same correlation structure is already used for synchronization, hence no additional hardware
complexity is required for the PDF estimation.
performance gained achieved over LS channel estimation is a significant, 2 dB.
profile vector is generated by maximizing PDF
maximization is considered for a single sub-carrier i.e. the peak value (at single sub-carrier) of PDF is
taken. statistically low significance it will be useful if we consider a number of sub-carrier’s PDF then
making an average of them. This will not increase the complexity (few adders) much but the technique
will be statistically sounder.
PDF calculation concerns a heuristic threshold factor K which makes the system particular
application/environment oriented. A statistical expression for K is recommended to make the system
environment independent.
MSE performance is reduced to 1.5dB. This performance could be increased by applying windowing
function as described in section 3.3 because the proposed technique is nothing but a generalized
approach of LS-DFT case.
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
16
Iterative Interpolation Channel Est. (1)
Ref: Keijo polonen and Koivunen, “Iterative Interpolation Method for Multiband-OFDM Channel Estimation”, IEEE
International Conference on Ultra-wideband (ICUWB), Singapore, September 2007.
generic approach for channel estimation is LS estimation at pilot sub-carriers followed by the
interpolation at others.
This method, however, exhibits significantly high error floor at the high SNR regime
^ p
h  S p R p    (15)
H
The channel of the kth sub-carrier may then be linearly calculated as
^ p
^
n ^
h(k )  h(m p  n)  h (m)  (h(m  1)  h(m))    (16)
m
^
^
^
^
h D1 (k )  hP1 (k ) 
^
h P2 (k )  h P1 (k )
   (17)
 p  2(i  1)
^
^
h D2 (k )  hP1 (k ) 
March 10, 2010
m p  k  (m  1) p
^
( p  2i  1)( h P2 (k )  h P1 (k ))
Telecommunication Engineering Lab, INHA Univ
 p  2(i  1)
   (18)
17
Iterative Interpolation Channel Est. (2)
Comments
The increase in complexity comes from the refinement of the channel estimate where just one complex
multiplication is needed for each of the sub-carriers
a performance gain of several dB at the high SNR regime with no error floor
BER performance is not increased and even substantially reduced at low SNR (error propagation which
affects the estimates of the sub-carriers further away from the pilot positions)
purpose of this technique is to improve the LS estimate and the availability of data is not a problem so
higher order derivative based interpolation would be explored and even to avoid additional complexity
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
18
Conclusion

MB-OFDM UWB: Channel Estimation Techniques

OLA: more robust at smaller OLA length

Time-frequency: relatively low complexity at high density channel

DFT-based: reduced spectral leakage

Adaptive: No additional hardware

Iterative Interpolation: No error floor at high SNR

?? Looking for improved channel Estimation technique
Thanks for your cooperation
Questions/Comments
March 10, 2010
Telecommunication Engineering Lab, INHA Univ
19