Evolutionary approach for optimal location area planning in mobile cellular network

Mobile cellular networks require proper planning of location areas (LA) which, would result in efficient resource utilization by resolving the issues like compromise between the signaling load generated in the network for performing location update and paging. The size of an LA could vary from one single cell to the entire service am under a Mobile Switching Center (MSC). These two situations of an LA depict two extreme possibilities. For the first case paging cost would be minimal whereas location update cost will be largest. For the law , the situation will be just the reverse. Fruitful pursuit possibility would be required towards finding out an optimum size of an LA within this spectrum such that, under the known patterns of call arrival and terminal mobility, total network cost, comprising of location update cost and cost for paging, can be minimized. In this work we have addressed the issue of inherent trade-off between these two cost components and have formulated a constrained cost optimization problem to find out an optimal LA size. Since the optimization problem is combinatorial in nature, as solution methodologies we have presented two heuristics, based on simulated annealing (SA) and genetic algorithm (GA) and have also compared the quality of solution using these two.