Heuristic approach for forecast scheduling
| Autor: | Eitan Altman, Zwi Altman, Hind Zaaraoui, Sana Ben Jemaa, Tania Jimenez |
|---|---|
| Přispěvatelé: | Orange Labs [Issy les Moulineaux], France Télécom, Orange Labs [Paris], Telecom Orange, Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Informatique d'Avignon (LIA), Centre d'Enseignement et de Recherche en Informatique - CERI-Avignon Université (AU), Altman, Zwi, Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI |
| Rok vydání: | 2018 |
| Předmět: |
geo-localized measurements
Forecast scheduler Mathematical optimization [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] Computer science Heuristic high mobility Signal-to-interference-plus-noise ratio 020206 networking & telecommunications Throughput 02 engineering and technology Radio Environment Maps Scheduling (computing) [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] alpha-fair Convex optimization 0202 electrical engineering electronic engineering information engineering Resource management |
| Zdroj: | WCNC Workshops IWSON 2018-7th International Workshop on Self-Organizing Networks IWSON 2018-7th International Workshop on Self-Organizing Networks, Apr 2018, Barcelona, Spain. pp.1-6 |
| DOI: | 10.1109/wcncw.2018.8369018 |
| Popis: | International audience; Forecast Scheduling (FS) is a scheduling concept that utilizes rate prediction along the users' trajectories in order to optimize the scheduler allocation. The rate prediction is based on Signal to Interference plus Noise Ratio (SINR) or rate maps provided by a Radio Environment Map (REM). The FS has been formulated as a convex optimization problem namely the maximization of an α−fair utility function of the cumulated rates of the users along their trajectories [1]. This paper proposes a fast heuristic for the FS problem based on two FS users' scheduling. Furthermore, it is shown that in the case of two users, the FS problem can be solved analytically, making the heuristic computationally very efficient. Numerical results illustrate the throughput gain brought about by the scheduling solution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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