Graph Coloring Algorithms and Applications to the Channel Assignment Problems

  • Surgwon Sohn
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 215)


This paper presents graph coloring algorithms and their applications to the channel assignment problems. Two application problems of frequency assignment of low power FM broadcasting and reader collision problem of RFID system are modeled as graph coloring problems. Instead of performing an exhaustive search for the optimal result, we provide both search space reduction and variable ordering heuristics to obtain good approximate solutions. Constraint optimization modeling and variable ordering enforce the backtracking process in graph coloring algorithms, so the search space is greatly reduced and the approximate solution is found quickly. A great deal of outstanding work on graph coloring algorithms is described and applied to the simulation results.


Graph coloring algorithms Channel assignment Variable ordering 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (KRF-2008-313- D00954)


  1. 1.
    Hale WK (1980) Frequency assignment: theory and applications. In: Proceedings of the IEEE, vol 68, pp 1497–1514Google Scholar
  2. 2.
    Giortzis AI, Turner LF (1997) Application of mathematical programming to the fixed channel assignment problem in mobile radio networks. In: IEEE Proceedings of Communication, vol 144, pp. 257–264Google Scholar
  3. 3.
    Carlsson M, Grindal M (1993) Automatic frequency assignment for cellular telephone using constraint satisfaction techniques. In: Proceedings of the tenth international conference on logic programming, pp 647–665Google Scholar
  4. 4.
    Walsher JP (1996) Feasible cellular frequency assignment using constraint programming abstractions. In: Proceedings of the workshop on constraint programming applications, CambridgeGoogle Scholar
  5. 5.
    Skiena S (1998) The Algorithm Design Manual, Springer, New YorkGoogle Scholar
  6. 6.
    Br′elaz D (1979) New methods to color the vertices of a graph. Commun ACM 22(4):251–256Google Scholar
  7. 7.
    Achlioptas D, Naor A (2005) The two possible values of the chromatic number of a random graph. Ann Math 162(3):1333–1349Google Scholar
  8. 8.
    Sohn S, Jo GS (2006) Solving a constraint satisfaction problem for frequency assignment in low power FM broadcasting. Lect Notes Artif Intell 4304:739–748Google Scholar
  9. 9.
    Zhou S, Luo Z, Wong E, Tan CJ, Luo J (2007) Interconnected RFID reader collision model and its application in reader anti-collision. In: Proceeding of the 2007 IEEE international conference on RFID, pp 212–219Google Scholar
  10. 10.
    Engels DW, Sarma SE (2002) The reader collision problem. In: Proceedings of the 2002 IEEE international conference on systems, man and cybernetics, pp 92–97Google Scholar
  11. 11.
    Sayeed SK, Kim YS, Yang H, Yook JG (2011) A solution to the RFID reader interference problem using adaptive beam-forming approach. IETE Tech Rev 28(1):17–28CrossRefGoogle Scholar
  12. 12.
    Song I, Hong S, Chang (2009) An improved reader Anti-collision algorithm based on pulse protocol with slot occupied probability in dense reader mode. In: Proceeding of the IEEE 69th vehicular technology conference, pp 1–5Google Scholar
  13. 13.
    Yu J, Lee W (2008) GENTLE: reducing reader collision in mobile RFID networks. In: Proceeding of the 4th international conference on mobile Ad-hoc and sensor networks, pp 280–287Google Scholar
  14. 14.
    Tian J, Fan Y, Zhu Y, Hu K (2008) RFID reader anti-collision using chaos neural network based on annealing strategy. In: Proceeding of the world congress on intelligent control and automation, pp. 6128–6132Google Scholar
  15. 15.
    Golomb SW, Baumert LD (1965) Backtrack programming. J ACM 12(4):516–524MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Haralick RM, Elliott GL (1980) Increasing tree search efficiency for constraint satisfaction problems. Artif Intell 14(3):263–313CrossRefGoogle Scholar
  17. 17.
    Freuder EC (1982) A sufficient condition for backtrack-free search. J ACM 29(1):24–32MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Bessi`ere C, R′egin J-C (1996) MAC and combined heuristics: two reasons to forsake FC (and CBJ?) on hard problems. Lect Notes Comput Sci 1118:61–75Google Scholar
  19. 19.
    Boussemart F, Hemery F, Lecoutre C Sais L (2004) Boosting systematic search by weighting constraints. In: Proceeding of the ECAI, pp 146–150Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Graduate School of VentureHoseo UniversitySeoulSouth Korea

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