The need for efficient spectrum utilization with reduced error rates has brought a paradigm shift in wireless communication systems from a Single Input and Single Output (SISO) systems to Multiple Input Multiple Output (MIMO) systems. Conventional diversity combiners are used to boost the received Signal to Noise Ratio at the Cognitive Radio receiver. However, these methods require perfect estimation of the channel. This paper proposes a Meta-Analytic approach based on p-Values for combining

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... es for combining the data received from a secondary user equipped with multiple antennas. The effect of the p-Value method as receiver diversity combiner is studied and is compared with the existing non-coherent combining schemes, which do not need channel state information. The weighted Z test and Fisher's method are used to combine the p-Values derived from the Anderson Darling (AD) and Jarque Bera (JB) test statistics. A ballpark figure of the merits of these diversity combining methods are provided in this study. Through extensive Monte Carlo simulations, it is shown that the weighted Z test using the Anderson Darling test statistic provides a probability of detection very close to the existing non-coherent diversity combiners. Hence, this novel statistical approach based on p-Values provides a promising solution to combine the test statistics from multiple receiver antennas.