From Distance to Angle: One-Shot Detection Under Additive White Cauchy Noise
This work addresses detection reliability under heavy-tailed noise for communication systems, presenting incremental theoretical insights.
The paper tackled one-shot detection under additive white Cauchy noise, deriving that symbol error probability depends on distance in low noise and on angular measures in high noise, with a four-point example illustrating the transition.
We study one-shot detection under additive white Cauchy noise (AWCN) using finite constellations, with emphasis on the geometric mechanisms governing symbol-level reliability. Under isotropic Cauchy noise, the maximum-likelihood rule induces the same Euclidean Voronoi decision regions as in the Gaussian case, so the distinction lies not in the decision geometry itself but in how probability mass is distributed over these fixed regions. In the small-noise regime, we derive a reciprocal distance-spectrum upper bound for the symbol error probability, showing that reliability retains a longer-range dependence on the global constellation geometry than under additive white Gaussian noise. In the large-noise regime, we prove that the correct-decision probability converges to a limit determined solely by the angular measure of the associated Voronoi recession cone. These results formalize a regime-dependent transition from distance-based to angle-based reliability descriptors under heavy-tailed noise. The theory is further illustrated through an asymmetric four-point example exhibiting geometric collapse and a standard 4QAM sanity check.