1. Joint Channel Estimation and Prediction for OFDM Systems
Ian C. Wong and Brian L. Evans
Embedded Signal Processing Laboratory
Wireless Networking and Communications Group
The University of Texas at Austin
IEEE Global Telecommunications Conference
Nov. 30, 2005 1

2. 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
Internet
Back haul
Base Station
Time-varying
Wideband Channel
Mobile
Feedback channel information 2

3. 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+) ? … 3

4. 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 4

5. Comparison of OFDM channel prediction approaches [Wong, Forenza, Heath, & Evans, 2004]
Compared three approaches in a unified framework
Complexity comparison 5

6. 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 6

7. System Model 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
Put legend when with equation 7

8. 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] 8

9. Pilot-based Transmission
Comb pilot pattern
Least-squares channel estimates t f … Dt Df
Get rid of pattern, legend of LS estimates, remind key parameters, Nt nf,… 9

10. 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 10

11. 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 steps
Time-delay estimation
Doppler-frequency estimation 11

12. 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] 12

13. 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 13

14. IEEE Simulation 14

15. Prediction Snapshot Predicted channel 1/5  ahead, SNR = 10 dB
Predicted channel trace, SNR = 10 dB
Predicted channel 1/5  ahead, SNR = 10 dB 15

16. MSE Performance 16

17. Summary L - No. of paths M - No. of rays per path
Explain last two lines more
L - No. of paths M - No. of rays per path 17