Abstract-The problem of distributed detection of a signal in incompletely specified noise is considered. The noise assumed belongs to the generalized Gaussian family and the sensors in the distributed network employ the Wilcoxon test. The sensors pass the test statistics to a fusion center, where a hypothesis testing results in a decision regarding the presence or the absence of a signal. Three monotone and admissible fusion center tests are formulated. Restricted numerical evaluation over a certain parameter range of the noise distribution and the range of signal level indicates that these tests yield performances at comparable levels.

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Growing demand in the wireless world puts plenty of constraints on the usage of accessible spectrum. Static Spectrum Allocation (SSA) ends up in underutilization of spectrum. Much of the portion of Radio Frequency (RF) band is not effectively used. Cognitive feature Radio is upcoming technology to unravel spectrum deficiency. Various approaches have been developed to overcome this spectrum scarcity problem. In this work Cyclostationary feature detection (CFD) and Energy Detection Spectrum sensing techniques have been investigated using Monte Carlo simulation and compared on the basis of various ROC parameters. Spectrum sensing is the key that helps to sight the spectrum holes (underutilized band) providing high spectral resolution capability. Cyclostationary feature methodology out performs Energy detection in terms of detection likelihood Pd, likelihood of warning Pf and SNR. Both the algorithms are implemented using MATLAB. The result shows that for different SNR the CFD performs better than Ed. CFD also perform better in low signal to noise ratio as compare to ED.

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The usefulness of SNR as a figure of merit to quantify the narrowband interference rejection capability of a DS receiver is examined. The interference considered is a peaked autoregressive Gaussian process. The probability of error and SNR estimates of a Kalman, a modified Kalman, and a nonlinear filter proposed in [2] are obtained by simulation. Based on this simulation study and the available theoretical error rate analysis of transversal filters, we can conclude that SNR is a useful measure if the processing gain, PG, of the DS system is moderately large. When the PG is small, such as 7, and if thermal noise is negligible compared to the signal, the