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Minimal missed detection rate of primary users is critical for adoption of cognitive radio networks, underlining the need for robust collaborative sensing combined with near-optimal single node detection methods. Although correlation based detection methods potentially provide needed per-node performance improvements for correlated signals, their performance for realistic blind sensing is unclear since the type and extent of correlation may be unknown in practice. Although standard Neymon-Pearson (NP) based detection can be applied when correlation is perfectly known, difficulty arises when the correlation is random, which is the focus of this paper.
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