Detection of Unknown Signals in a Fading Environment

[attachment=17352]

INTRODUCTION
The energy detection is a common approach to
decide whether unknown signals exist in the
medium. The first step of the detector design
requires a model for the distribution of the noise
and the signal. It is reasonable to describe the noise
as a simple white Gaussian process. The signal
model has to be more complex to incorporate the
fading effects.


ILLUSTRATIONS
We illustrate the usage of (13) by computing the
miss probability of the energy detector. The miss
probability is described by the CDF of the decision
variable L. We compute the CDF by integrating
(13) numerically. In the computations we assumed
that the slow fading has mean SNR μdB = 5 and
standard deviation σdB = 3. The size of the block,
the number of collected power samples, is N =
1000. The predictions made by the model are
compared to the simulation results.


CONCLUSION
In this paper we proposed a model that
describes the signal power distribution in fast/slow
fading environment. The model allowed deriving
the distribution of the decision variable in energy
detection. With this distribution at hand, we could
easily predict the detector performance. The
proposed model is a useful tool for studying the
detection performance in different fading
environments. We illustrated that by comparing the
detection performance in the fast/slow fading
environment with the fast fading environment.
We found that if the mean signal power does not
change during the measured block of the signal
samples, the simple fast fading model describes the
detector performance quite well. The slow fading
becomes more important if multiple blocks with
different fast fading values are combined.