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Multi-User collaborative scheduling in 5G massive MIMO heterogeneous networks

Authors :
Masson, Marie
Altman, Zwi
Altman, Eitan
Altman, Eitan
Orange Labs [Chatillon]
Orange Labs
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)
Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI
Laboratory of Information, Network and Communication Sciences (LINCS)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT)-Sorbonne Université (SU)
Source :
IFIP Networking 2020, IFIP Networking 2020, Jun 2020, Paris / Online, France
Publication Year :
2020
Publisher :
HAL CCSD, 2020.

Abstract

International audience; Macro cell densification with Small Cells (SCs) is an effective solution to cope with traffic increase. To fully benefit from the additional SCs capacity, interference mitigation techniques are needed. Densification in 5G networks with Massive Multiple Input Multiple Output (M-MIMO) deployment needs to rethink interference mitigation to account for highly focused beams and MultiUser (MU) scheduling. This paper presents a low complexity collaborative Proportional Fair (PF) based scheduling that maximizes the throughput and improves fairness of the heterogeneous network. The solution is based on the calculation of a loss factor indicator that each SC provides to the macro cell at each scheduling period. These indicators allow the macro cell MU scheduler to efficiently select the set of users for scheduling, leading to a significant improvement in performance. Numerical results illustrate the interest of the collaborative solution.

Details

Language :
English
Database :
OpenAIRE
Journal :
IFIP Networking 2020, IFIP Networking 2020, Jun 2020, Paris / Online, France
Accession number :
edsair.dedup.wf.001..9d0a008529206a263c8ec174d327dea0