Statistical Characterization of URLLC : Frequentist and Bayesian Approaches

The stringent, and often extreme, reliability guarantees posed for ultra-reliable low-latency communications (URLLC) can be attained and claimed in a credible way only by relying on a proper statistical characterization. This characterization should justify the claim that a user will be guaranteed a reliability of, say, 99.999 %, given the observed data on the wireless channel as well as any other prior knowledge. The chapter presents the statistical aspects of URLLC, detailing both frequentist and Bayesian approaches. URLLC has different definitions for reliability, availability, latency, etc., depending on which communication layers are included. This chapter focuses on the physical layer since it is fundamental to the understanding of the overall performance. Specifically, we are analyzing the statistical features and guarantees for outage probability in a narrowband wireless channel. Outage can occur when the instantaneous channel quality cannot support the data rate selected by the transmitter. For the frequentist approach, there is a need for excessive number of observations required to characterize ultra-rare events. The required number of observations can be decreased by using prior knowledge, which brings in the Bayesian method. As a motivating example, we treat the practical case in which a base station (BS) collects channel statistics for users at different locations and attempts to predict the performance of a user at a new location. We show how this case can be addressed by using statistical characterization of radio maps. The approach has been tested on a synthetic dataset generated by raytracing. It has been shown that the BS can obtain high-quality predictions of the reliability performance even for locations that are not in proximity.