  Ultra-reliable communication (URC) is a key enabler for supporting immersive
and mission-critical 5G applications. Meeting the strict reliability
requirements of these applications is challenging due to the absence of
accurate statistical models tailored to URC systems. In this letter, the
wireless connectivity over dynamic channels is characterized via statistical
learning methods. In particular, model-based and data-driven learning
approaches are proposed to estimate the non-blocking connectivity statistics
over a set of training samples with no knowledge on the dynamic channel
statistics. Using principles of survival analysis, the reliability of wireless
connectivity is measured in terms of the probability of channel blocking
events. Moreover, the maximum transmission duration for a given reliable
non-blocking connectivity is predicted in conjunction with the confidence of
the inferred transmission duration. Results show that the accuracy of detecting
channel blocking events is higher using the model-based method for low to
moderate reliability targets requiring low sample complexity. In contrast, the
data-driven method shows higher detection accuracy for higher reliability
targets at the cost of 100$\times$ sample complexity.
