Topics: Radio resource allocation/management; Wireless multi-access and communication
Authors: Farah Moety, Mustapha Bouhtou, Taouk En-Najjary and Ridha Nasri (Orange Labs, France)
Presenter Bio: Farah Moety is currently a network planification and optimization
engineer at Orange, Lyon, France. she was a post-doctoral fellow
researcher in Orange Labs, Chatillon, France. She obtained her Ph.D.
degree in 2014 from University of Rennes1, Rennes, France. She was a
member of ATNET research team at IRISA Labs, Rennes, France. She
obtained her Master 2 in telecommunication networks in 2011 jointly from
Lebanese University (EDST) and Saint-Joseph University (ESIB) in
Lebanon. She has done her master thesis at IRISA Labs, Rennes, France.
She received her Telecommunication and Computer Engineering diploma from
Lebanese University, Faculty of Engineering III in 2010, Beirut,
Lebanon. Her research interests include green wireless access networks,
radio resource allocation, heterogeneous networks, LTE networks, WLANs,
network optimization, radio access technology selection, FTTH.
In this paper, we address the joint problem
of user association and resource allocation in wireless heterogeneous
networks. Therefore, we formulate an optimization approach considering
two objectives, namely, maximizing the number of served User Equipments
(UEs) and maximizing the sum of the UE utilities. Precisely, the aim is
to associate UEs with the optimal Radio Access Technology (RAT) and to
allocate to these UEs the optimal Resource Units (RUs) based on their
requested services and contracts. Our problem is challenging because it
is mixed integer non-linear optimization. To tackle this difficulty, we
provide a Mixed Integer Linear Programming (MILP) re-formulation of the
problem that makes it computationally tractable. Various preferences for
user association and resource allocation are conducted by tuning: on
the one hand, the weights associated with different services and
contracts; on the other hand, the weights associated with the considered
two objectives. The optimal solution of the MILP problem is computed
for a realistic network scenario and compared with legacy solution.
Extensive simulation results show that the proposed optimization
approach improves the overall network performance while considering the
UE requested service and contract: it outperforms legacy solutions in
distribution of UEs on the different RATs.