Research Article
A Multi-objective Optimization Approach for Designing Multihop Cellular Networks
@INPROCEEDINGS{10.1007/978-3-642-29222-4_29, author={Souha Bannour and Abdelhakim Hafid and Mariam Tagmouti}, title={A Multi-objective Optimization Approach for Designing Multihop Cellular Networks}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Networks. 7th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2010, and Dedicated Short Range Communications Workshop, DSRC 2010, Houston, TX, USA, November 17-19, 2010, Revised Selected Papers}, proceedings_a={QSHINE}, year={2012}, month={10}, keywords={Multihop Cellular Networks Design problem Multi-objective optimization}, doi={10.1007/978-3-642-29222-4_29} }- Souha Bannour
Abdelhakim Hafid
Mariam Tagmouti
Year: 2012
A Multi-objective Optimization Approach for Designing Multihop Cellular Networks
QSHINE
Springer
DOI: 10.1007/978-3-642-29222-4_29
Abstract
A proper design of multi-hop cellular networks (MCNs) is a key step before its deployment. It helps determining where to install the nodes and how to configure their interfaces while guaranteeing full user coverage and satisfying traffic and QoS requirements with minimum cost.Few proposals can be found in the open literature that deals with the MCN design problem. Furthermore, these proposals assume the existence of a physical topology where the locations of the nodes are fixed.In this paper, we consider the design of MCNs assuming unfixed topologies (i.e., locations of nodes are not known a priori). We start with proposing a new multi-objective optimization model for designing MCNs. This model simultaneously optimizes two conflicting objectives, namely network deployment cost and throughput while guaranteeing users’ full coverage and the requirements of providers (expected amount of traffic/users and QoS)To resolve the optimization problem, we start with an exact resolution using CPLEX, and then we develop a fast and simple greedy algorithm.