joint channel estimation and prediction for ofdm systems
DESCRIPTION
Joint Channel Estimation and Prediction for OFDM Systems. Ian C. Wong and Brian L. Evans {iwong,bevans}@ece.utexas.edu Embedded Signal Processing Laboratory Wireless Networking and Communications Group The University of Texas at Austin IEEE Global Telecommunications Conference Nov. 30, 2005. - PowerPoint PPT PresentationTRANSCRIPT
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Joint Channel Estimation and Prediction for OFDM Systems
Ian C. Wong and Brian L. Evans{iwong,bevans}@ece.utexas.edu
Embedded Signal Processing LaboratoryWireless Networking and Communications Group
The University of Texas at AustinIEEE Global Telecommunications Conference
Nov. 30, 2005
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Adaptive Orthogonal Frequency Division Multiplexing (OFDM)
Adjust transmission based on channel information Maximize data rates and/or improve link quality
Problems Feedback delay - significant performance loss Volume of feedback - power and bandwidth overhead
InternetBack haul
Base Station
Time-varying Wideband Channel
Mobile
Feedback channel information
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Prediction of Wireless Channels Use current and previous channel estimates to
predict future channel response Overcome feedback delay
Adaptation based on predicted channel response Lessen amount of feedback
Predicted channel response may replace direct channel feedback
h(n-p)h(n-)
h(n)h(n+) ?
…
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Related Work Prediction on each subcarrier [Forenza & Heath, 2002]
Each subcarrier treated as a narrowband autoregressive process [Duel-Hallen et al., 2000]
Prediction using pilot subcarriers [Sternad & Aronsson, 2003]
Used unbiased power prediction [Ekman, 2002]
Prediction on time-domain channel taps [Schafhuber & Matz, 2005]
Used adaptive prediction filters
… …
Pilot Subcarriers
Data Subcarriers
IFFT
Time-domain channel taps
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Compared three approaches in a unified framework
Complexity comparison
Comparison of OFDM channel prediction approaches
[Wong, Forenza, Heath, & Evans, 2004]
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Summary of Main Contributions
Formulated OFDM channel prediction problem as a 2-dimensional frequency estimation problem
Proposed a 2-step 1-dimensional prediction approach Lower complexity with minimal performance loss Rich literature of 1-D sinusoidal parameter
estimation Allows decoupling of computations between
receiver and transmitter
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OFDM baseband received signal Perfect timing and carrier synchronization and inter-
symbol interference elimination by the cyclic prefix Flat passband for transmit and receiver filters over
used subcarriers
System Model
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Deterministic Channel Model Outdoor mobile macrocell scenario
Far-field scatterer (plane wave assumption) Linear motion with constant velocity Small time window (a few wavelengths)
Used in modeling and simulation of wireless channels [Jakes 74], ray-tracing channel characterization [Rappaport 02]
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Comb pilot pattern
Least-squares channel estimates
Pilot-based Transmission
t
f …
Dt
Df
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Prediction via 2-D Frequency Estimation
If we accurately estimate parameters in our channel model, we could effectively extrapolate the fading process
Estimation and extrapolation period should be within time window where model parameters are stationary
A two-dimensional complex sinusoids in noise estimation Well studied in radar, sonar, and other array signal
processing applications [Kay, 1988]
A lot of algorithms available, but are computationally prohibitive
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Two-step One-dimensional Frequency Estimation
Typically, a lot of propagation paths share the same resolvable time delay
We can thus break down the problem into two steps1. Time-delay estimation
2. Doppler-frequency estimation
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Step 1 – Time-delay estimation
Estimate autocorrelation function using the modified covariance averaging method [Stoica & Moses, 1997]
Estimate the number of paths L using minimum description length rule [Xu, Roy, & Kailath, 1994]
Estimate the time delays using Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) [Roy & Kailath, 1989]
Estimate the amplitudes cp(l) using least-squares Discrete Fourier Transform of these amplitudes could be
used to estimate channel More accurate than conventional approaches, and similar to
parametric channel estimation method in [Yang, et al., 2001]
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Step 2 – Doppler freq. estimation
Using complex amplitudes cp(l) estimated from Step 1 as the left hand side for (2), we determine the rest of the parameters
Similar steps as Step 1 can be applied for the parameter estimation for each path p
Using the estimated parameters, predict channel as
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IEEE 802.16 Simulation
0 0.5 1 1.5 2 2.5 3
x 10-6
0
0.1
0.2
0.3
0.4
0.5
Time delay
Pa
th p
ow
er
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Prediction SnapshotPredicted channel 1/5 ahead, SNR = 10 dB
Predicted channel trace, SNR = 10 dB
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MSE Performance
10 15 20 25 30 35-50
-45
-40
-35
-30
-25
-20
-15
-10
-5Prediction Normalized MSE 2 ahead
SNR in dB
Nor
maliz
ed M
SE
in d
B
0.2 0.4 0.6 0.8 1-16
-15
-14
-13
-12
-11
-10
-9
-8Prediction Normalized MSE, SNR=10 dB
Prediction length ()
Nor
maliz
ed M
SE
in d
B7.484 8.4044
-12.6799
-11.5004
Proposed Method, ML Estimates
Proposed Method, MMSE Estimates
Burg Prediction
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Summary
L - No. of paths M - No. of rays per path