Cognitive Radio: A Non-parametric Approach by Approximating Signal Plus Noise Distribution by Kaplansky Distributions in the Context of Spectrum Hole Search
Cognitive Radio has been acknowledged to be the ultimate solution to meet the huge spectrum demand due to various state-of-the-art communication technologies. It exploits the underutilized frequency band of the legacy users for the unlicensed users opportunistically. This requires a sensible spectrum sensing technique, generally performed by binary hypotheses testing. Noise and signal plus noise distributions are important in this context. These are assumed to be Gaussian in the suboptimal energy detection technique whereas these assumptions may not be validated by practical data. In this paper, the signal plus noise distribution is approximated by four distributions, known as Kaplansky distributions that closely resemble with Gaussian distribution. Testing of hypothesis is performed by non-parametric Kolmogorov Smirnov test and power of the test is calculated for a specific false alarm probability. Numerical results are provided in support of our proposition.
KeywordsCognitive Radio Kaplansky distributions non-parametric Kolmogorov Smirnov test power of test false alarm probability
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