Location area planning problem plays an important role in cellular networks because of the trade-off caused by paging and registration signalling (i.e., location update). Compromising between the location update and the paging costs is essential in order to improve the performance of the network. The trade-off between these two factors can be optimized in such a way that the total cost of paging and location update can be minimized along with the link cost. Due to the complexity of this problem, meta-heuristic techniques are often used for analysing and solving practical sized instances. In this paper, we propose an approach to solve the LA planning problem based on the Particle Swarm Optimization (PSO) algorithm. The performance of the approach is investigated and evaluated with respect to the solution quality on a range of problem instances. Moreover, experimental work demonstrated the performance comparison in terms of different degree of mobility, paging load, call traffic load, and TRX load. The performance of the proposed approach outperform other existing meta-heuristic based approaches for the most problem instances.
Particle Swarm Optimization, Simulated Annealing Optimization, Ant Colony Optimization, Location Management in Cellular Networks, Swarm Intelligence.