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Adaptive Modulation Systems for Predicted Wireless Channels.
Sorour Falahati,
Uppsala University,
Arne Svensson,
Chalmers U. of Technology,
Mikael Sternad,
Uppsala University and
Torbjörn Ekman
UNIK, Norway.
IEEE Globecom,
San Fransisco, Califonia, Dec. 2003, pp. 357-371.
© IEEE
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Abstract:
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When adaptive modulation is used to counter
short-term fading in mobile radio channels,
signaling delays create problems with outdated
channel state information. The use of channel
power prediction will improve the performance
of the link adaptation. It is then of interest to take
the quality of these predictions into account
explicitly when designing an adaptive modulation scheme.
We study the optimum design of an adaptive modulation
scheme based on uncoded M-QAM modulation
assisted by channel prediction for the
flat Rayleigh fading channel.
The data rate, and in some variants the transmit power,
are adapted to maximize the spectral efficiency
subject to average power and bit error rate constraints.
The key issues studied here are how a known
prediction error variance will affect the optimized
transmission properties such as the SNR boundaries
that determine when to apply different modulation rates,
and to what extent it affects the spectral efficiency.
This investigation is performed by analytical
optimization of the link adaptation, using the
statistical properties of a particular but efficient
channel power predictor.
Optimum solutions for the rate and transmit power
are derived based on the predicted SNR and
the prediction error variance.
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Related publications:
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Journal paper version,
(IEEE TCOM 2004).
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Paper at VTC2003,
comparing also to trellis-coded modulation (TCM).
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Channel Power Prediction,
by using unbiased predictors and
advanced regressor noise reduction (VTC 2002-Fall).
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PhD Thesis on channel prediction,
by Torbjörn Ekman, where derivations of the
statistics of the prediction errors can be found.
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Proc. of the IEEE (Dec. 2007)
invited paper on
adaptive transmission in beyond-3G wireless systems.
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Source:
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Pdf, (299K)
Postscript (242K)
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