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Prediction of Fading Broadband Wireless Channels. JOINT BEATS/Wireless IP seminar, Loen. Torbjörn Ekman UniK-University Graduate Center Oslo, Norway. Contents. Motivation Noise Reduction Linear Prediction of Channels Delay Spacing, Sub-sampling Results Power Prediction Results

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Prediction of fading broadband wireless channels

Prediction of Fading Broadband Wireless Channels

JOINT BEATS/Wireless IP seminar, Loen

Torbjörn Ekman

UniK-University Graduate Center

Oslo, Norway


Contents
Contents

  • Motivation

  • Noise Reduction

  • Linear Prediction of Channels

  • Delay Spacing, Sub-sampling

  • Results

  • Power Prediction

  • Results

  • Recommendations


Prediction of fading broadband wireless channels
Why?

With channels known in advance the problem with fast fading can be turned into an advantage

  • Adaptive resource allocation

  • Fast link adaptation

    The multi-user diversity can be exploited


Prediction of fading broadband wireless channels

Noise Reduction of Estimated Channels

The estimated Doppler spectrum is low pass and has a noise floor.

The same noise floor is seen in the power delay profile.



Prediction of fading broadband wireless channels

FIR or IIR Wiener-smoother?

  • IIR smoothers

  • based on a low pass ARMA-model

  • can be numerically sensitive

  • need few parameters

  • FIR smoothers

  • based on a model for the covariance

  • need many parameters

  • Both have similar performance.

  • Both use estimates of the variance of the estimation error and the Doppler frequency.


Prediction of fading broadband wireless channels

Linear Prediction of Mobile Radio Channels

  • A step towards power prediction

  • Can produce prediction of the frequency response

  • Model for the tap

  • The FIR-predictor

  • The MSE-optimal coefficients





Prediction of fading broadband wireless channels

The MSE optimal delay spacing for the Jakes model depends on the variance of the estimation error.

The NMSE has many local minima.


Sub sampling and aliasing
Sub the variance of the estimation error.-sampling and aliasing

  • OSR 50

  • Sub-sampling rate 13

  • Jakes model

  • SNR 10dB

  • 16 predictor coefficients

  • FIR Wiener smoother (128)


Prediction of fading broadband wireless channels

Prediction performance on the variance of the estimation error.a Jakes model

  • OSR 50 (100 samples per l)

  • FIR predictor, 8 coefficients

  • FIR Wiener smoother (128)

  • Dashed lines: no smoother


The measurements
The Measurements the variance of the estimation error.

  • Channel sounder measurements in urban and suburban Stockholm

  • Carrier frequency 1880MHz

  • Baseband sampling rate 6.4MHz

  • Channel update rate 9.1kHz

  • Vehicle speeds 30-90km/h

  • 1430 consecutive impulse responses at each location

  • Data from 41 measurement locations


Prediction performance on the taps
Prediction performance on the taps the variance of the estimation error.


Channel prediction performance
Channel prediction performance the variance of the estimation error.


Prediction of fading broadband wireless channels

Power Prediction the variance of the estimation error.

  • The power of a tap

  • A biased quadratic predictor

  • An unbiased quadratic predictor

  • Rayleigh fading taps: the optimal q for the complex tap prediction is optimal also for the power prediction.


Biased and unbiased nmse
Biased and unbiased NMSE the variance of the estimation error.


Prediction of fading broadband wireless channels

Observed power or complex regressors? the variance of the estimation error.

  • AR2-process

  • Approx. Jakes

  • FIR predictor (2)

  • Dash-dotted line for observed power in the regressors.


Power prediction performance
Power prediction performance the variance of the estimation error.


Median tap prediction performance
Median the variance of the estimation error.tap prediction performance


Channel prediction
Channel prediction the variance of the estimation error.


Compare average predictor with unbiased predictor
Compare average predictor with unbiased predictor the variance of the estimation error.


Predictor design
Predictor Design the variance of the estimation error.

  • Estimate the channel with uttermost care.

  • Noise reduction using Wiener smoothers.

  • Estimate sub-sampled AR-models or use a direct FIR-predictor.

  • Estimate as few parameters as possible.

  • Design Kalman predictor using a noise model that compensates for estimation errors

  • Power prediction: Squared magnitude of tap prediction with added bias compensation.