Did you also hear that? Spectrum sensing using Hermitian inner product

Spectrum sensing is one of the most important enabling techniques on which to build a cognitive radio network. However, previously proposed techniques often have shortcomings in non-ideal environments: 1) An energy detector is simple but cannot perform in face of uncertain noise power; 2) A matched filter is the optimal detector, but performs poorly with clock drifts; 3) Eigenvalue-based blind feature detectors show great promise, but cannot detect signals that are noise-like; and 4) Above protocols all rely on field survey to determine the proper decision thresholds. We propose HIPSS and its extension Δ-HIPSS that are based on the Hermitian-inner-product of two observations acquired by a wireless receiver over multiple radio paths. HIPSS and Δ-HIPSS are lightweight and through extensive analysis and evaluation, we show that 1) HIPSS and Δ-HIPSS are robust in the presence of noise power uncertainties; 2) HIPSS and Δ-HIPSS require neither a much longer observation duration nor complex computation compared to an energy detector in ideal setting; 3) HIPSS and Δ-HIPSS can detect noise-like primary signals; and 4) Δ-HIPSS can reliably return sensing decisions without necessitating any field surveys.