By scheduling users to resources that are operational rather than on a best effort basis, the overall resource consumption of ultra-reliable low-latency communications (URLLC) can be reduced while maintaining a desired quality of service (QoS). To overcome the time delay between monitoring the channel state and the actual payload transmission, predictive methods which are tailored to the needs of URLLC become indispensable. In this paper we extend our Wiener filter based Rayleigh fading outage predictor to the Rician fading case. Compared to Rayleigh fading, additional estimators for the line of sight (LOS) parameters are presented. Our results show that the overall outage prediction performance increases significantly with increasing power of the LOS component compared to the Rayleigh fading case. The resource utilization for a particular user equipment (UE) rises to more than 99% in the investigated scenario for small prediction horizons and a Rician K-factor of K = 10 while achieving effective outage probabilities of 10 −5 . By comparing with the case of perfect parameter estimation, we show that the influence of the introduced estimators on the outage prediction performance is within acceptable limits.