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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
slide3
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

slide4

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.

slide6

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.
slide7

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
slide11

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-sampling and aliasing
  • OSR 50
  • Sub-sampling rate 13
  • Jakes model
  • SNR 10dB
  • 16 predictor coefficients
  • FIR Wiener smoother (128)
slide13

Prediction performance on 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
  • 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
slide17

Power Prediction

  • 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.
slide19

Observed power or complex regressors?

  • AR2-process
  • Approx. Jakes
  • FIR predictor (2)
  • Dash-dotted line for observed power in the regressors.
predictor design
Predictor Design
  • 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.
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