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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