AccessNets. Third International Conference on Access Networks, AccessNets 2008, Las Vegas, NV, USA, October 15-17, 2008. Revised Papers

Research Article

Modeling of Channel Allocation in Broadband Powerline Communications Access Networks as a Multi-Criteria Optimization Problem

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  • @INPROCEEDINGS{10.1007/978-3-642-04648-3_14,
        author={Abdelfatteh Haidine and Ralf Lehnert},
        title={Modeling of Channel Allocation in Broadband Powerline Communications Access Networks as a Multi-Criteria Optimization Problem},
        proceedings={AccessNets. Third International Conference on Access Networks, AccessNets 2008, Las Vegas, NV, USA, October 15-17, 2008. Revised Papers},
        proceedings_a={ACCESSNETS},
        year={2012},
        month={5},
        keywords={Channel allocation broadband powerline communications access network planning multi-criteria optimization evolutionary algorithms},
        doi={10.1007/978-3-642-04648-3_14}
    }
    
  • Abdelfatteh Haidine
    Ralf Lehnert
    Year: 2012
    Modeling of Channel Allocation in Broadband Powerline Communications Access Networks as a Multi-Criteria Optimization Problem
    ACCESSNETS
    Springer
    DOI: 10.1007/978-3-642-04648-3_14
Abdelfatteh Haidine1,*, Ralf Lehnert1
  • 1: Dresden University of Technology
*Contact email: haidine@ifn.et.tu-dresden.de

Abstract

The planning process of the Broadband Powerline communications access networks contains two main problem parts: the (GBSP) problem and the (P-CAP). The GBSP is investigated/solved in our previous works. In this paper, we focus on the P-CAP. The task of the P-CAP consists in allocating a sub-set of channels from an available set of PLC channels to each base station in the B-PLC site. Two optimization objectives are considered for the solution of this problem; namely the maximization of the resource reuse and the minimization of the generated interferences in the site. These objectives are conflicting, since the optimization of one of them results in the deterioration of the other. Therefore, this problem is modeled as a Multi-objective (or multi-criteria) Optimization Problem (MOP). Three variants of Pareto-based multi-objective algorithms, using evolutionary search, are used to solve it. Their performances are evaluated on four problem instances.