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Joint Channel Estimation and Prediction for OFDM Systems

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.

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Joint Channel Estimation and Prediction for OFDM Systems

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  1. 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 1

  2. 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 Time-varying Wideband Channel Back haul Internet Mobile Base Station Feedback channel information Adaptive Orthogonal Frequency Division Multiplexing (OFDM) 2

  3. h(n-p) h(n-) h(n+) ? h(n) … 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 3

  4. Pilot Subcarriers IFFT … … Data Subcarriers Time-domain channel taps 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 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 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. … f Df t Dt Pilot-based Transmission • Comb pilot pattern • Least-squares channel estimates 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 802.16 Simulation 14

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

  16. MSE Performance 16

  17. Summary L - No. of paths M - No. of rays per path 17

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