Abstract
Many spectrum sensing algorithms have as a purpose determining if the primary users (PUs) are present or absent, by using statistical signal characteristics. However, for a better management of a cognitive network, additional information about PUs, such as their position, is required. Motivated by these requirements, this paper investigate the performance of MUSIC and Capon algorithms in Cognitive Radio networks context and show that the MUSIC algorithm is highly accurate and stable while also providing a high angular resolution compared to the Capon algorithm.
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