Title :
Fixed channel assignment in cellular radio networks using a modified genetic algorithm
Author :
Ngo, Chiu Y. ; Li, Victor O K
Author_Institution :
Philips Res. Lab., Briancliff Manor, NY., USA
fDate :
2/1/1998 12:00:00 AM
Abstract :
With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict-free channel assignment with the minimum channel span is NP hard. Therefore, we formulate the problem by assuming a given channel span. Our objective is to obtain a conflict-free channel assignment among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. We propose an approach based on a modified genetic algorithm (GA). The approach consists of a genetic-fix algorithm that generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and a minimum-separation encoding scheme that eliminates redundant zeros in the solution representation. Using these two strategies, the search space can be reduced substantially. Simulations on the first four benchmark problems showed that this algorithm could achieve at least 80%, if not 100%, convergence to solutions within reasonable time. In the fifth benchmark problem, our algorithm found better solutions with shorter channel span than any existing algorithms. Such significant results indicate that our approach is indeed a good method for solving the channel-assignment problem
Keywords :
cellular radio; channel coding; computational complexity; frequency allocation; genetic algorithms; land mobile radio; radio spectrum management; search problems; EMC; NP hard; benchmark problems; binary representation; cellular radio networks; conflict-free channel assignment; convergence; electromagnetic compatibility; fixed channel assignment; frequency spectrum; genetic-fix algorithm; minimum channel span; minimum-separation encoding scheme; modified genetic algorithm; redundant zeros; search space; solution representation; traffic demand; Cellular networks; Electromagnetic compatibility; Frequency; Genetic algorithms; Intelligent networks; Land mobile radio cellular systems; Neural networks; Simulated annealing; Telecommunication traffic; Wireless communication;
Journal_Title :
Vehicular Technology, IEEE Transactions on