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Article

Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ

1
Video Communication and Applications Department, Fraunhofer Heinrich-Hertz-Institute, Einsteinufer 37, 10587 Berlin, Germany
2
Applied Informatics and Probability Department, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya St. 6, 117198 Moscow, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Daniel-Ioan Curiac
Mathematics 2021, 9(17), 2104; https://doi.org/10.3390/math9172104
Received: 28 June 2021 / Revised: 26 July 2021 / Accepted: 23 August 2021 / Published: 31 August 2021
(This article belongs to the Special Issue Applications of Mathematical Analysis in Telecommunications)
In order to fulfill the stringent Ultra-Reliable Low Latency Communication (URLLC) requirements towards Fifth Generation (5G) mobile networks, early-Hybrid Automatic Repeat reQuest (e-HARQ) schemes have been introduced, aimed at providing faster feedback and thus earlier retransmission. The performance of e-HARQ prediction strongly depends on the classification mechanism, data length, threshold value. In this paper, we propose an analytical model that incorporates e-HARQ and Hybrid Automatic Repeat reQuest (HARQ) functionalities in terms of two phases in discrete time. The model implies a fast and accurate way to get the main performance measures, and apply optimization analysis to find the optimal values used in predictor’s classification. We employ realistic data for transition probabilities obtained by means of 5G link-level simulations and conduct extensive experimental analysis. The results show that at false positive probability of 101, the e-HARQ prediction with the found optimal parameters can achieve around 20% of gain over HARQ at False Negative (FN) of 101 and around 7.5% at FN of 103 in terms of a mean spending time before successful delivery. View Full-Text
Keywords: Markov chain model; stationary distribution; performance measures; 5G mobile communication; HARQ; early-HARQ Markov chain model; stationary distribution; performance measures; 5G mobile communication; HARQ; early-HARQ
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MDPI and ACS Style

Rykova, T.; Göktepe, B.; Schierl, T.; Samouylov, K.; Hellge, C. Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ. Mathematics 2021, 9, 2104. https://doi.org/10.3390/math9172104

AMA Style

Rykova T, Göktepe B, Schierl T, Samouylov K, Hellge C. Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ. Mathematics. 2021; 9(17):2104. https://doi.org/10.3390/math9172104

Chicago/Turabian Style

Rykova, Tatiana, Barış Göktepe, Thomas Schierl, Konstantin Samouylov, and Cornelius Hellge. 2021. "Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ" Mathematics 9, no. 17: 2104. https://doi.org/10.3390/math9172104

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