Analytical model and feedback predictor optimization for combined early-harq and harq

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 10−1, the e-HARQ prediction with the found optimal parameters can achieve around 20% of gain over HARQ at False Negative (FN) of 10−1 and around 7.5% at FN of 10−3 in terms of a mean spending time before successful delivery. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Авторы
Rykova T. 1 , Göktepe B.1 , Schierl T.1 , Samouylov K. 2 , Hellge C.1
Журнал
Издательство
MDPI AG
Номер выпуска
17
Язык
Английский
Статус
Опубликовано
Номер
2104
Том
9
Год
2021
Организации
  • 1 Video Communication and Applications Department, Fraunhofer Heinrich-Hertz-Institute, Einsteinufer 37, Berlin, 10587, Germany
  • 2 Applied Informatics and Probability Department, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya St. 6, Moscow, 117198, Russian Federation
Ключевые слова
5G mobile communication; Early-HARQ; HARQ; Markov chain model; Performance measures; Stationary distribution
Дата создания
16.12.2021
Дата изменения
16.12.2021
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/76676/
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