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

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 6)


The planning process of the Broadband Powerline communications access networks contains two main problem parts: theGeneralized Base Station Placement (GBSP) problem and the PLC Channel Allocation Problem (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.


Channel allocation broadband powerline communications access network planning multi-criteria optimization evolutionary algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Haidine, A., Mellado, I., Lehnert, R.: PANDeMOO: Powerline communications access network designer based on multi-objective optimisation. In: 9th ISPLC, VC, Canada (2005)Google Scholar
  2. 2.
    Open PLC European Research Alliance (OPERA),
  3. 3.
    Hale, W.K.: Frequency assignment: theory and applications. Proceeding of the IEEE 68(12), 1497–1514 (1980)CrossRefGoogle Scholar
  4. 4.
    Maniezzo, V., Carbonaro, A.: An ANTS Heuristic for the Frequency Assignment Problem. Future Generation Computer Systems 16, 927–935 (2000)CrossRefGoogle Scholar
  5. 5.
    Koster, A.: Frequency Assignment - Models and Algorithms. Dissertation at University of Maastricht, Netherlands (1999)Google Scholar
  6. 6.
    Aardal, K., et al.: Models and Solution Techniques for Frequency Assignment Problems. Konrad-Zuse-Zentrum für Informationstechnik Berlin –Report 01-40 (2001)Google Scholar
  7. 7.
    Schulz, M.: Solving Frequency Assignment Problems with Constraint Programming. Diploma thesis at Institute for Mathematics, Berlin University of Technology (February 2003)Google Scholar
  8. 8.
    Haidine, A., Lehnert, R.: Analysis of the Channel Allocation Problem in Broadband Power Line Communications Access Networks. In: 11th IEEE ISPLC, Pisa, Italy (2007)Google Scholar
  9. 9.
    Eisenblätter, A.: Frequency Assignment in GSM Networks: Models, Heuristics, and Lower Bounds. Dissertation at Berlin University of technology, Berlin (2001)Google Scholar
  10. 10.
    Beckmann, D.: Algorithmen zur Planung und Optimierung moderner Kommunikationsnetze. PhD at Hamburg-Harburg University of Technology (2003)Google Scholar
  11. 11.
    Maniezzo, V., Montemanni, R.: An exact algorithm for the min-interference frequency assignment problem. Research Report WP-CO0003, University of Bologna, Italy (2000)Google Scholar
  12. 12.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  13. 13.
    Deb, K.: A Fast and Elitist Multiobjective genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Comput. 6(2) (2002)Google Scholar
  14. 14.
    Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD Thesis, Swiss Federal Institute of Technology (1999)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  1. 1.Chair for TelecommunicationsDresden University of TechnologyDresdenGermany

Personalised recommendations