Overcoming SNR walls through macroscale features

We consider the problem of robustly detecting the presence/absence of signals in low signal to noise ratio (SNR) environments. Our previous results have shown the existence of thresholds called SNR walls, below which robust detection becomes impossible due to uncertainties in the environment. These thresholds were shown to exist for many signals used in practice. This paper introduces the idea of macroscale features and shows that they can be used to construct signals that evade SNR walls. In particular, we examine a Gaussian mixture example and a noise-calibration based detection algorithm to show that this signal can be robustly detected in the presence of arbitrarily-varying noise and arbitrarily-varying finite-tap fading processes. Finally, we argue that there is tension between the primary user´s capacity and the sensing delay experienced by the secondary users. We call this the capacity-delay tradeoff. We derive the capacity-delay tradeoff for the Gaussian example.