Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

TL;DR

This paper introduces survival analysis-based statistical learning methods to model and predict ultra-reliable wireless connectivity, addressing the challenge of accurately estimating non-blocking event probabilities in dynamic channels for 5G applications.

Contribution

It proposes both model-based and data-driven approaches using survival analysis to estimate connectivity reliability without prior channel statistics knowledge.

Findings

Model-based method performs better for low to moderate reliability with fewer samples.

Data-driven approach achieves higher accuracy for high reliability but requires significantly more data.

The methods enable prediction of maximum transmission duration with confidence levels.

Abstract

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.