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Analytical model of early HARQ feedback prediction

: Rykova, T.; Göktepe, B.; Schierl, T.; Hellge, C.


Galinina, O.:
Internet of Things, Smart Spaces, and Next Generation Networks and Systems. 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020. Proceedings. Pt.II : St. Petersburg, Russia, August 26-28, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12526)
ISBN: 978-3-030-65728-4 (Print)
ISBN: 978-3-030-65729-1 (Online)
ISBN: 978-3-030-65730-7
International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems (NEW2AN) <20, 2020, Online>
Conference on the Internet of Things and Smart Spaces (ruSMART) <13, 2020, Online>
Fraunhofer HHI ()

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.