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Detection of Unknown Signals in a Fading Environment - rvd - 10-04-2017 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 dB = 5 and standard deviation dB = 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. |