Prediction Error-Based Model Predictive Control for Resource Allocation of 5G Ultra-reliable Low-Latency Communication

5G ultra-reliable low-latency communication (uRLLC) requires extremely low latency and high reliability to serve safety-critical user ends (UEs) and applications. To fulfill those requirements, many uRLLC-related tasks are simplified for Quality of Service (QoS) analysis. Commonly Poisson or Bernoulli distributions are assumed for the incoming traffic. However, both distributions can only roughly present the characteristics of most communication traffic. On the other hand, the analysis of QoS according to predictions of traffic also requires further research. In this work, we consider the existence of a predictor for the incoming traffic and take the cumulative density function (CDF) of prediction errors into uRLLC’s QoS discussions. Furthermore, we consider a typical uRLLC resource allocation task and apply model predictive control (MPC) by converting the QoS into constraints of an optimization problem. The simulations shows that MPC can provide good performance with the prediction module, enhancing a robust operation and mitigating the stochastic effects of environmental conditions.