On Performance Comparison of Wideband Multiple Primary User Detection Methods in Cognitive Radios

One of the key tasks in cognitive radio (CR) system is to dynamically explore the spectrum and reliably detect the presence of primary users (PU). In this paper, we revisit several classical spectrum analysis methods, such as, weighted overlapped segment averaging (WOSA) approach, multi-taper spectral estimator (MTSE) and multiple signal classification (MUSIC) algorithm into wideband multiple primary user sensing and detection. We propose to use the polyphase DFT filter banks (DFB) for the implementation of multi-taper spectral estimator which achieves a good tradeoff between complexity and performance. Detailed performance and computational complexity comparisons of these methods are provided.