Analytical Model of Early HARQ Feedback Prediction.

We propose analytical model that investigates early Hybrid Automatic Repeat reQuest (HARQ) prediction scheme as a path towards Ultra-Reliable Low Latency Communication (URLLC). By incorporating early-HARQ (e-HARQ) and HARQ functionalities in terms of two phases in a model, we can evaluate the performance of their parallel processing. Moreover, we perform comparative analysis of the e-HARQ model with a random predictor model and a model that covers a traditional HARQ approach. We show a benefit of e-HARQ model in terms of various performance measures. We employ realistic data for transition probabilities obtained by means of 5G link-level simulations into e-HARQ model to get the evaluations of the main performance measures, such as false-negative and false-positive probabilities, in a fast and accurate way. The proposed model can be used as an efficient tool to get a quick estimate of the performance measures when selecting a classification-based parameter in an e-HARQ mechanism.