Title :
Channel assignment using genetic algorithm based on geometric symmetry
Author :
Ghosh, Sasthi C. ; Sinha, Bhabani P. ; Das, Nabanita
Author_Institution :
Adv. Comput. & Microelectron. Unit, Indian Stat. Inst., Kolkata, India
fDate :
7/1/2003 12:00:00 AM
Abstract :
The paper deals with the channel assignment problem in a hexagonal cellular network with two-band buffering, where channel interference does not extend beyond two cells. Here, for cellular networks with homogeneous demands, we find some lower bounds on the minimum bandwidth required for various relative values of s0, s1, and s2, the minimum frequency separations to avoid interference for calls in the same cell, or in cells at distances of one and two, respectively. We then present an algorithm for solving the channel assignment problem in its general form using the elitist model of genetic algorithm (EGA). We next apply this technique to the special case of hexagonal cellular networks with two-band buffering. For homogeneous demands, we apply EGA for assigning channels to a small subset of nodes and then extend it for the entire cellular network, which ensures faster convergence. Moreover, we show that our approach is also applicable to cases of nonhomogeneous demands. Application of our proposed methodology to well-known benchmark problems generates optimal results within a reasonable computing time.
Keywords :
adjacent channel interference; cellular radio; channel allocation; cochannel interference; frequency allocation; genetic algorithms; graph colouring; adjacent channel interference; benchmark problems; channel assignment; cochannel interference; computing time; cosite interference; elitist genetic algorithm model; frequency assignment; generalized graph-coloring problem; geometric symmetry; hexagonal cellular network; minimum bandwidth; minimum frequency separation; two-band buffering; Bandwidth; Convergence; Frequency conversion; Genetic algorithms; Interference constraints; Land mobile radio cellular systems; Microelectronics; NP-complete problem; Neural networks; Simulated annealing;
Journal_Title :
Vehicular Technology, IEEE Transactions on
DOI :
10.1109/TVT.2003.808806