prediction of fading broadband wireless channels
Post on 01-Feb-2016
37 Views
Preview:
DESCRIPTION
TRANSCRIPT
Prediction of Fading Broadband Wireless Channels
Torbjörn Ekman
UniK-University Graduate Center
Oslo, Norway
JOINT BEATS/Wireless IP seminar, Loen
Contents
• Motivation• Noise Reduction• Linear Prediction of Channels• Delay Spacing, Sub-sampling• Results• Power Prediction• Results• Recommendations
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
Why?
Noise Reduction of Estimated Channels
The same noise floor is seen in the power delay profile.
The estimated Doppler spectrum is low pass and has a noise floor.
IIR smoothers
FIR or IIR Wiener-smoother?
• IIR smoothers1. based on a low pass ARMA-model2. can be numerically sensitive3. need few parameters• FIR smoothers1. based on a model for the covariance2. need many parameters• Both have similar performance.• Both use estimates of the variance of the
estimation error and the Doppler frequency.
Linear Prediction of Mobile Radio Channels
• Model for the tap
• The FIR-predictor
• The MSE-optimal coefficients
• A step towards power prediction
• Can produce prediction of the frequency response
Linear prediction with noise reduction
Model Based Prediction
Delay Spacing
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
• OSR 50
• Sub-sampling rate 13
• Jakes model
• SNR 10dB
• 16 predictor coefficients
• FIR Wiener smoother (128)
Prediction performance on a Jakes model
• OSR 50 (100 samples per )
• FIR predictor, 8 coefficients
• FIR Wiener smoother (128)
• Dashed lines: no smoother
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
Prediction performance on the taps
Channel prediction performance
Power Prediction
• The power of a tap
• A biased quadratic predictor
• An unbiased quadratic predictor
• Rayleigh fading taps: the optimal for the complex tap prediction is optimal also for the power prediction.
Biased and unbiased NMSE
Observed power or complex
regressors?
• AR2-process
• Approx. Jakes
• FIR predictor (2)
• Dash-dotted line for observed power in the regressors.
Power prediction performance
Median tap prediction performance
Channel prediction
Compare average predictor with unbiased predictor
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.
top related