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Abstract-Spectrum sensing is a key ingredient of the dynamic spectrum access paradigm, but it needs powerful detectors operating at SNRs well below the decodability levels of primary signals. Noise uncertainty poses a significant challenge to the development of such schemes, requiring some degree of diversity (spatial, temporal, or in distribution) for identifiability of the noise level. Multiantenna detectors exploit spatial independence of receiver thermal noise. We review this class of schemes and propose a novel detector trading off performance and complexity. However, most of these methods assume that the noise power, though unknown, is the same at all antennas. As it turns out, calibration errors have a substantial impact on these detectors. Another novel detector is proposed, based on an approximation to the Generalized Likelihood Ratio, outperforming previous schemes for uncalibrated multiantenna receivers.

We propose a saddlepoint approximation of the error probability of a binary hypothesis test between two i.i.d. distributions. The approximation is accurate, simple to compute, and yields a unified analysis in different asymptotic regimes. The proposed formulation is used to efficiently compute the metaconverse lower bound for moderate block-lengths in several cases of interest.

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